polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

shao-etal-2022-formlm

{F}orm{LM}: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.557/

Shao, Yijia and Zhou, Mengyu and Zhong, Yifan and Wu, Tao and Han, Hongwei and Han, Shi and Huang, Gideon and Zhang, Dongmei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8133--8149

Online forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where questions and descriptions are organized by predefined structures. However, the design and creation process of forms is still tedious and requires expert knowledge. To assist form designers, in this work we present FormLM to model online forms (by enhancing pre-trained language model with form structural information) and recommend form creation ideas (including question / options recommendations and block type suggestion). For model training and evaluation, we collect the first public online form dataset with 62K online forms. Experiment results show that FormLM significantly outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively.

null

10.18653/v1/2022.emnlp-main.557

null

27,687

inproceedings

chen-etal-2022-generate

Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.558/

Chen, Yiming and Zhang, Yan and Wang, Bin and Liu, Zuozhu and Li, Haizhou

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8150--8161

Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performance of unsupervised methods typically lags far behind that of the supervised counterparts in most downstream tasks. In this work, we propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data. Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data. Comprehensive experiments show that GenSE achieves an average correlation score of 85.19 on the STS datasets and consistent performance improvement on four domain adaptation tasks, significantly surpassing the state-of-the-art methods and convincingly corroborating its effectiveness and generalization ability.

null

10.18653/v1/2022.emnlp-main.558

null

27,688

inproceedings

xu-etal-2022-gps

{GPS}: Genetic Prompt Search for Efficient Few-Shot Learning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.559/

Xu, Hanwei and Chen, Yujun and Du, Yulun and Shao, Nan and Yanggang, Wang and Li, Haiyu and Yang, Zhilin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8162--8171

Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the manual design of prompts and thus requiring a lot of human efforts. In this paper, we introduce Genetic Prompt Search (GPS) to improve few-shot learning with prompts, which utilizes a genetic algorithm to automatically search for the best prompt.GPS is gradient-free and requires no update of model parameters but only a small validation set. Experiments on diverse datasets proved the effectiveness of GPS, which outperforms manual prompts by a large margin of 2.6 points. Our method is also better than other parameter-efficient tuning methods such as prompt tuning.

null

10.18653/v1/2022.emnlp-main.559

null

27,689

inproceedings

alhindi-etal-2022-multitask

Multitask Instruction-based Prompting for Fallacy Recognition

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.560/

Alhindi, Tariq and Chakrabarty, Tuhin and Musi, Elena and Muresan, Smaranda

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8172--8187

Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. Recognizing fallacies is an intrinsically difficult task both for humans and machines. Moreover, a big challenge for computational models lies in the fact that fallacies are formulated differently across the datasets with differences in the input format (e.g., question-answer pair, sentence with fallacy fragment), genre (e.g., social media, dialogue, news), as well as types and number of fallacies (from 5 to 18 types per dataset). To move towards solving the fallacy recognition task, we approach these differences across datasets as multiple tasks and show how instruction-based prompting in a multitask setup based on the T5 model improves the results against approaches built for a specific dataset such as T5, BERT or GPT-3. We show the ability of this multitask prompting approach to recognize 28 unique fallacies across domains and genres and study the effect of model size and prompt choice by analyzing the per-class (i.e., fallacy type) results. Finally, we analyze the effect of annotation quality on model performance, and the feasibility of complementing this approach with external knowledge.

null

10.18653/v1/2022.emnlp-main.560

null

27,690

inproceedings

xiao-etal-2022-rethinking

Rethinking Multi-Modal Alignment in Multi-Choice {V}ideo{QA} from Feature and Sample Perspectives

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.561/

Xiao, Shaoning and Chen, Long and Gao, Kaifeng and Wang, Zhao and Yang, Yi and Zhang, Zhimeng and Xiao, Jun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8188--8198

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA). The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisticated architectures while utilizing frame- or object-level visual representations. In this paper, we reconsider the multi-modal alignment problem in VideoQA from feature and sample perspectives to achieve better performance. From the view of feature, we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities. Moreover, we adopt a heterogeneous graph architecture and design a hierarchical framework to align both trajectory-level and frame-level visual feature with language feature. In addition, we found that VideoQA models are largely dependent on languagepriors and always neglect visual-language interactions. Thus, two effective yet portable training augmentation strategies are designed to strengthen the cross-modal correspondence ability of our model from the view of sample. Extensive results show that our method outperforms all the state-of the-art models on the challenging NExT-QA benchmark.

null

10.18653/v1/2022.emnlp-main.561

null

27,691

inproceedings

chen-etal-2022-towards-table

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.562/

Chen, Miao and Lu, Xinjiang and Xu, Tong and Li, Yanyan and Jingbo, Zhou and Dou, Dejing and Xiong, Hui

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8199--8210

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables. Large-scale pretrained language models sound like a promising solution to tackle such issues. However, how to effectively bridge the gap between the structured table and the text input by fully leveraging table information to fuel the pretrained model is still not well explored. Besides, another challenge of integrating the deliberation mechanism into the text-to-text pretrained model for solving the table-to-text task remains seldom studied. In this paper, to implement the table-to-text generation with pretrained language model, we propose a table structure understanding and text deliberating approach, namely TASD. To be specific, we devise a three-layered multi-head attention network to realize the table-structureaware text generation model with the help of the pretrained language model. Furthermore, a multi-pass decoder framework is adopted to enhance the capability of polishing generated text for table descriptions. The empirical studies, as well as human evaluation, on two public datasets, validate that our approach can generate faithful and fluent descriptive texts for different types of tables.

null

10.18653/v1/2022.emnlp-main.562

null

27,692

inproceedings

wang-etal-2022-hierarchical-phrase

Hierarchical Phrase-Based Sequence-to-Sequence Learning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.563/

Wang, Bailin and Titov, Ivan and Andreas, Jacob and Kim, Yoon

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8211--8229

This paper describes a neural transducer that maintains the flexibility of standard sequence-to-sequence (seq2seq) models while incorporating hierarchical phrases as a source of inductive bias during training and as explicit constraints during inference. Our approach trains two models: a discriminative parser based on a bracketing transduction grammar whose derivation tree hierarchically aligns source and target phrases, and a neural seq2seq model that learns to translate the aligned phrases one-by-one. We use the same seq2seq model to translate at all phrase scales, which results in two inference modes: one mode in which the parser is discarded and only the seq2seq component is used at the sequence-level, and another in which the parser is combined with the seq2seq model. Decoding in the latter mode is done with the cube-pruned CKY algorithm, which is more involved but can make use of new translation rules during inference. We formalize our model as a source-conditioned synchronous grammar and develop an efficient variational inference algorithm for training. When applied on top of both randomly initialized and pretrained seq2seq models, we find that it performs well compared to baselines on small scale machine translation benchmarks.

null

10.18653/v1/2022.emnlp-main.563

null

27,693

inproceedings

sprague-etal-2022-natural

Natural Language Deduction with Incomplete Information

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.564/

Sprague, Zayne and Bostrom, Kaj and Chaudhuri, Swarat and Durrett, Greg

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8230--8258

A growing body of work studies how to answer a question or verify a claim by generating a natural language {\textquotedblleft}proof:{\textquotedblright} a chain of deductive inferences yielding the answer based on a set of premises. However, these methods can only make sound deductions when they follow from evidence that is given. We propose a new system that can handle the underspecified setting where not all premises are stated at the outset; that is, additional assumptions need to be materialized to prove a claim. By using a natural language generation model to abductively infer a premise given another premise and a conclusion, we can impute missing pieces of evidence needed for the conclusion to be true. Our system searches over two fringes in a bidirectional fashion, interleaving deductive (forward-chaining) and abductive (backward-chaining) generation steps. We sample multiple possible outputs for each step to achieve coverage of the search space, at the same time ensuring correctness by filtering low-quality generations with a round-trip validation procedure. Results on a modified version of the EntailmentBank dataset and a new dataset called Everyday Norms: Why Not? Show that abductive generation with validation can recover premises across in- and out-of-domain settings.

null

10.18653/v1/2022.emnlp-main.564

null

27,694

inproceedings

chen-etal-2022-character

Character-centric Story Visualization via Visual Planning and Token Alignment

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.565/

Chen, Hong and Han, Rujun and Wu, Te-Lin and Nakayama, Hideki and Peng, Nanyun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8259--8272

Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. This task requires machines to 1) understand long text inputs, and 2) produce a globally consistent image sequence that illustrates the contents of the story. A key challenge of consistent story visualization is to preserve characters that are essential in stories. To tackle the challenge, we propose to adapt a recent work that augments VQ-VAE with a text-to-visual-token (transformer) architecture. Specifically, we modify the text-to-visual-token module with a two-stage framework: 1) character token planning model that predicts the visual tokens for characters only; 2) visual token completion model that generates the remaining visual token sequence, which is sent to VQ-VAE for finalizing image generations. To encourage characters to appear in the images, we further train the two-stage framework with a character-token alignment objective. Extensive experiments and evaluations demonstrate that the proposed method excels at preserving characters and can produce higher quality image sequences compared with the strong baselines.

null

10.18653/v1/2022.emnlp-main.565

null

27,695

inproceedings

stelmakh-etal-2022-asqa

{ASQA}: Factoid Questions Meet Long-Form Answers

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.566/

Stelmakh, Ivan and Luan, Yi and Dhingra, Bhuwan and Chang, Ming-Wei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8273--8288

Recent progress on open domain factoid question answering (QA) does not easily transfer to the task of long-form QA, where the goal is to answer questions that require in-depth explanations. The hurdles include a lack of high-quality data and the absence of a well-defined notion of an answer`s quality. In this work, we address these problems by releasing a novel dataset and a task that we call ASQA (Answer Summaries for Questions which are Ambiguous); and proposing a reliable metric for measuring performance on ASQA. Our task focuses on ambiguous factoid questions which have different correct answers depending on the interpretation. Answers to ambiguous questions should combine factual information from multiple sources into a coherent long-form summary that resolves the ambiguity. In contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear notion of correctness: a user faced with a good summary should be able to answer different interpretations of the original ambiguous question. Our analysis demonstrates an agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines.

null

10.18653/v1/2022.emnlp-main.566

null

27,696

inproceedings

svete-etal-2022-algorithms

Algorithms for Acyclic Weighted Finite-State Automata with Failure Arcs

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.567/

Svete, Anej and Dayan, Benjamin and Cotterell, Ryan and Vieira, Tim and Eisner, Jason

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8289--8305

Weighted finite-state automata (WSFAs) arecommonly used in NLP. Failure transitions area useful extension for compactly representingbackoffs or interpolation in n-gram modelsand CRFs, which are special cases of WFSAs.Unfortunately, applying standard algorithmsfor computing the pathsum requires expand-ing these compact failure transitions. As aresult, na {\ensuremath{\ddot{}}}{\i}ve computation of the pathsum inacyclic WFSAs with failure transitions runs inO(|Q|2|{\ensuremath{\Sigma}}|) (O(|Q||{\ensuremath{\Sigma}}|) for deterministic WF-SAs) while the equivalent algorithm in normalWFSAs runs in O(|E|), where E representsthe set of transitions, Q the set of states, and{\ensuremath{\Sigma}} the alphabet. In this work, we present moreefficient algorithms for computing the pathsumin sparse acyclic WFSAs, i.e., WFSAs with av-erage out symbol fraction s {\ensuremath{\ll}} 1. In those,backward runs in O(s|Q||{\ensuremath{\Sigma}}|). We proposean algorithm for semiring-weighted automatawhich runs in O(|E| + s|{\ensuremath{\Sigma}}||Q||Tmax| log |{\ensuremath{\Sigma}}|),where |Tmax| is the size of the largest con-nected component of failure transitions. Ad-ditionally, we propose faster algorithms fortwo specific cases. For ring-weighted WF-SAs we propose an algorithm with complex-ity O(|E| + s|{\ensuremath{\Sigma}}||Q||{\ensuremath{\pi}}max|), where |{\ensuremath{\pi}}max| de-notes the longest path length of failure transi-tions stemming from q and {\ensuremath{\Sigma}}(q) the set of sym-bols on the outgoing transitions from q. Forsemiring-weighted WFSAs whose failure tran-sition topology satisfies a condition exemplifiedby CRFs, we propose an algorithm with com-plexity O(|E| + s|{\ensuremath{\Sigma}}||Q| log |{\ensuremath{\Sigma}}|).

null

10.18653/v1/2022.emnlp-main.567

null

27,697

inproceedings

zhang-etal-2022-towards-better

Towards Better Document-level Relation Extraction via Iterative Inference

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.568/

Zhang, Liang and Su, Jinsong and Chen, Yidong and Miao, Zhongjian and Zijun, Min and Hu, Qingguo and Shi, Xiaodong

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8306--8317

Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose relations can only be predicted through relational inference. Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs. To deal with this issue, in this paper, we propose a novel document-level RE model with iterative inference. Our model is mainly composed of two modules: 1) a base module expected to provide preliminary relation predictions on entity pairs; 2) an inference module introduced to refine these preliminary predictions by iteratively dealing with difficultly-predicted entity pairs depending on other pairs in an easy-to-hard manner. Unlike previous methods which only consider feature information of entity pairs, our inference module is equipped with two Extended Cross Attention units, allowing it to exploit both feature information and previous predictions of entity pairs during relational inference. Furthermore, we adopt a two-stage strategy to train our model. At the first stage, we only train our base module. During the second stage, we train the whole model, where contrastive learning is introduced to enhance the training of inference module. Experimental results on three commonly-used datasets show that our model consistently outperforms other competitive baselines.

null

10.18653/v1/2022.emnlp-main.568

null

27,698

inproceedings

xi-etal-2022-efficient

Efficient Adversarial Training with Robust Early-Bird Tickets

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.569/

Xi, Zhiheng and Zheng, Rui and Gui, Tao and Zhang, Qi and Huang, Xuanjing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8318--8331

Adversarial training is one of the most powerful methods to improve the robustness of pre-trained language models (PLMs). However, this approach is typically more expensive than traditional fine-tuning because of the necessity to generate adversarial examples via gradient descent. Delving into the optimization process of adversarial training, we find that robust connectivity patterns emerge in the early training phase (typically 0.15{\textasciitilde}0.3 epochs), far before parameters converge. Inspired by this finding, we dig out robust early-bird tickets (i.e., subnetworks) to develop an efficient adversarial training method: (1) searching for robust tickets with structured sparsity in the early stage; (2) fine-tuning robust tickets in the remaining time. To extract the robust tickets as early as possible, we design a ticket convergence metric to automatically terminate the searching process. Experiments show that the proposed efficient adversarial training method can achieve up to $7\times \sim 13 \times$ training speedups while maintaining comparable or even better robustness compared to the most competitive state-of-the-art adversarial training methods.

null

10.18653/v1/2022.emnlp-main.569

null

27,699

inproceedings

mireshghallah-etal-2022-quantifying

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.570/

Mireshghallah, Fatemehsadat and Goyal, Kartik and Uniyal, Archit and Berg-Kirkpatrick, Taylor and Shokri, Reza

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8332--8347

The wide adoption and application of Masked language models (MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities. Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying potential robustness of MLMs to privacy attacks.In this work, we posit that prior attempts were inconclusive because they based their attack solely on the MLM`s model score. We devise a stronger membership inference attack based on likelihood ratio hypothesis testing that involves an additional reference MLM to more accurately quantify the privacy risks of memorization in MLMs. We show that masked language models are indeed susceptible to likelihood ratio membership inference attacks: Our empirical results, on models trained on medical notes, show that our attack improves the AUC of prior membership inference attacks from 0.66 to an alarmingly high 0.90 level.

null

10.18653/v1/2022.emnlp-main.570

null

27,700

inproceedings

mohammadshahi-etal-2022-small

{SM}a{LL}-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.571/

Mohammadshahi, Alireza and Nikoulina, Vassilina and Berard, Alexandre and Brun, Caroline and Henderson, James and Besacier, Laurent

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8348--8359

In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation. To overcome the {\textquotedblleft}curse of multilinguality{\textquotedblright}, these models often opt for scaling up the number of parameters, which makes their use in resource-constrained environments challenging. We introduce SMaLL-100, a distilled version of the M2M-100(12B) model, a massively multilingual machine translation model covering 100 languages. We train SMaLL-100 with uniform sampling across all language pairs and therefore focus on preserving the performance of low-resource languages. We evaluate SMaLL-100 on different low-resource benchmarks: FLORES-101, Tatoeba, and TICO-19 and demonstrate that it outperforms previous massively multilingual models of comparable sizes (200-600M) while improving inference latency and memory usage. Additionally, our model achieves comparable results to M2M-100 (1.2B), while being 3.6x smaller and 4.3x faster at inference.

null

10.18653/v1/2022.emnlp-main.571

null

27,701

inproceedings

zhou-etal-2022-textfusion

{T}ext{F}usion: Privacy-Preserving Pre-trained Model Inference via Token Fusion

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.572/

Zhou, Xin and Lu, Jinzhu and Gui, Tao and Ma, Ruotian and Fei, Zichu and Wang, Yuran and Ding, Yong and Cheung, Yibo and Zhang, Qi and Huang, Xuanjing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8360--8371

Recently, more and more pre-trained language models are released as a cloud service. It allows users who lack computing resources to perform inference with a powerful model by uploading data to the cloud. The plain text may contain private information, as the result, users prefer to do partial computations locally and upload intermediate representations to the cloud for subsequent inference.However, recent studies have shown that intermediate representations can also be recovered to plain text with reasonable accuracy, thus the risk of privacy leakage still exists. To address this issue, we propose TextFusion, a novel method for preserving inference privacy.Specifically, we train a Fusion Predictor to dynamically fuse token representations, which hides multiple private token representations behind an unrecognizable one.Furthermore, an adversarial training regime is employed to privatize these representations. In this way, the cloud only receives incomplete and perturbed representations, making it difficult to accurately recover the complete plain text.The experimental results on diverse classification tasks show that our approach can effectively preserve inference privacy without significantly sacrificing performance in different scenarios.

null

10.18653/v1/2022.emnlp-main.572

null

27,702

inproceedings

feng-boyd-graber-2022-learning

Learning to Explain Selectively: A Case Study on Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.573/

Feng, Shi and Boyd-Graber, Jordan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8372--8382

Explanations promise to bridge the gap between humans and AI, yet it remains difficult to achieve consistent improvement in AI-augmented human decision making. The usefulness of AI explanations depends on many factors, and always showing the same type of explanation in all cases is suboptimal{---}so is relying on heuristics to adapt explanations for each scenario. We propose learning to explain{\textquotedblright}selectively{\textquotedblright}: for each decision that the user makes, we use a model to choose the best explanation from a set of candidates and update this model with feedback to optimize human performance. We experiment on a question answering task, Quizbowl, and show that selective explanations improve human performance for both experts and crowdworkers.

null

10.18653/v1/2022.emnlp-main.573

null

27,703

inproceedings

li-etal-2022-consisttl

{C}onsist{TL}: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.574/

Li, Zhaocong and Liu, Xuebo and Wong, Derek F. and Chao, Lidia S. and Zhang, Min

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8383--8394

Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods for NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization. In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model. Specifically, for each training instance of the child model, ConsistTL constructs the semantically-equivalent instance for the parent model and encourages prediction consistency between the parent and child for this instance, which is equivalent to the child model learning each instance under the guidance of the parent model. Experimental results on five low-resource NMT tasks demonstrate that ConsistTL results in significant improvements over strong transfer learning baselines, with a gain up to 1.7 BLEU over the existing back-translation model on the widely-used WMT17 Turkish-English benchmark. Further analysis reveals that ConsistTL can improve the inference calibration of the child model. Code and scripts are freely available at https://github.com/NLP2CT/ConsistTL.

null

10.18653/v1/2022.emnlp-main.574

null

27,704

inproceedings

haghighatkhah-etal-2022-better

Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.575/

Haghighatkhah, Pantea and Fokkens, Antske and Sommerauer, Pia and Speckmann, Bettina and Verbeek, Kevin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8395--8416

Bias elimination and recent probing studies attempt to remove specific information from embedding spaces. Here it is important to remove as much of the target information as possible, while preserving any other information present. INLP is a popular recent method which removes specific information through iterative nullspace projections.Multiple iterations, however, increase the risk that information other than the target is negatively affected.We introduce two methods that find a single targeted projection: Mean Projection (MP, more efficient) and Tukey Median Projection (TMP, with theoretical guarantees). Our comparison between MP and INLP shows that (1) one MP projection removes linear separability based on the target and (2) MP has less impact on the overall space.Further analysis shows that applying random projections after MP leads to the same overall effects on the embedding space as the multiple projections of INLP. Applying one targeted (MP) projection hence is methodologically cleaner than applying multiple (INLP) projections that introduce random effects.

null

10.18653/v1/2022.emnlp-main.575

null

27,705

inproceedings

wang-etal-2022-ielm

{IELM}: An Open Information Extraction Benchmark for Pre-Trained Language Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.576/

Wang, Chenguang and Liu, Xiao and Song, Dawn

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8417--8437

We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer {\textquotedblleft}fill-in-the-blank{\textquotedblright} questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zero-shot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets.

null

10.18653/v1/2022.emnlp-main.576

null

27,706

inproceedings

zhou-etal-2022-conner

{C}on{NER}: Consistency Training for Cross-lingual Named Entity Recognition

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.577/

Zhou, Ran and Li, Xin and Bing, Lidong and Cambria, Erik and Si, Luo and Miao, Chunyan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8438--8449

Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distillation methods attempt to bridge the language gap, but often introduce a high level of noise. To solve this problem, consistency training methods regularize the model to be robust towards perturbations on data or hidden states.However, such methods are likely to violate the consistency hypothesis, or mainly focus on coarse-grain consistency.We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropout-based consistency training on labeled source-language data. ConNER effectively leverages unlabeled target-language data and alleviates overfitting on the source language to enhance the cross-lingual adaptability. Experimental results show our ConNER achieves consistent improvement over various baseline methods.

null

10.18653/v1/2022.emnlp-main.577

null

27,707

inproceedings

xie-etal-2022-sequential

A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.578/

Xie, Minghui and Hao, Chuzhan and Zhang, Peng

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8450--8460

One of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning. Since in different hops, one attends to different parts of question, it is important to dynamically represent the question semantics for each hop. Existing methods, however, (i) infer the dynamic question representation only through coarse-grained attention mechanisms, which may bring information loss, (ii) and have not effectively modeled the sequential logic, which is crucial for the multi-hop reasoning process in KBQA.To address these issues, we propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each single hop in a more fine-grained way. Based on Gated Recurrent Unit (GRU) which is good at modeling sequential process, we propose a simple but effective GRU-inspired Flow Control (GFC) framework to model sequential logic in the whole multi-hop process.Extensive experiments on three popular benchmark datasets have demonstrated the superior effectiveness of our model. In particular, GFC achieves new state-of-the-art Hits@1 of 76.8{\%} on WebQSP and is also effective when KB is incomplete. Our code and data are available at https://github.com/Xie-Minghui/GFC.

null

10.18653/v1/2022.emnlp-main.578

null

27,708

inproceedings

hao-etal-2022-acenet

{ACEN}et: Attention Guided Commonsense Reasoning on Hybrid Knowledge Graph

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.579/

Hao, Chuzhan and Xie, Minghui and Zhang, Peng

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8461--8471

Augmenting pre-trained language models (PLMs) with knowledge graphs (KGs) has demonstrated superior performance on commonsense reasoning. Given a commonsense based QA context (question and multiple choices), existing approaches usually estimate the plausibility of candidate choices separately based on their respective retrieved KGs, without considering the interference among different choices. In this paper, we propose an Attention guided Commonsense rEasoning Network (ACENet) to endow the neural network with the capability of integrating hybrid knowledge. Specifically, our model applies the multi-layer interaction of answer choices to continually strengthen correct choice information and guide the message passing of GNN. In addition, we also design a mix attention mechanism of nodes and edges to iteratively select supporting evidence on hybrid knowledge graph. Experimental results demonstrate the effectiveness of our proposed model through considerable performance gains across CommonsenseQA and OpenbookQA datasets.

null

10.18653/v1/2022.emnlp-main.579

null

27,709

inproceedings

tan-etal-2022-revisiting

Revisiting {D}oc{RED} - Addressing the False Negative Problem in Relation Extraction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.580/

Tan, Qingyu and Xu, Lu and Bing, Lidong and Ng, Hwee Tou and Aljunied, Sharifah Mahani

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8472--8487

The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE). It adopts a recommend-revise annotation scheme so as to have a large-scale annotated dataset. However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent. We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset. To address the shortcoming, we re-annotate 4,053 documents in the DocRED dataset by adding the missed relation triples back to the original DocRED. We name our revised DocRED dataset Re-DocRED. We conduct extensive experiments with state-of-the-art neural models on both datasets, and the experimental results show that the models trained and evaluated on our Re-DocRED achieve performance improvements of around 13 F1 points. Moreover, we conduct a comprehensive analysis to identify the potential areas for further improvement.

null

10.18653/v1/2022.emnlp-main.580

null

27,710

inproceedings

ravaut-etal-2022-towards

Towards Summary Candidates Fusion

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.581/

Ravaut, Mathieu and Joty, Shafiq and Chen, Nancy

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8488--8504

Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam. Recently, re-ranking methods have been proposed, to learn to select a better summary candidate. However, such methods are limited by the summary quality aspects captured by the first-stage candidates. To bypass this limitation, we propose a new paradigm in second-stage abstractive summarization called SummaFusion that fuses several summary candidates to produce a novel abstractive second-stage summary. Our method works well on several summarization datasets, improving both the ROUGE scores and qualitative properties of fused summaries. It is especially good when the candidates to fuse are worse, such as in the few-shot setup where we set a new state-of-the art. We will make our code and checkpoints available at https://github.com/ntunlp/SummaFusion/.

null

10.18653/v1/2022.emnlp-main.581

null

27,711

inproceedings

zhao-calapodescu-2022-multimodal

Multimodal Robustness for Neural Machine Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.582/

Zhao, Yuting and Calapodescu, Ioan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8505--8516

In this paper, we look at the case of a Generic text-to-text NMT model that has to deal with data coming from various modalities, like speech, images, or noisy text extracted from the web. We propose a two-step method, based on composable adapters, to deal with this problem of Multimodal Robustness. In a first step, we separately learn domain adapters and modality specific adapters, to deal with noisy input coming from various sources: ASR, OCR, or noisy text (UGC). In a second step, we combine these components at runtime via dynamic routing or, when the source of noise is unknown, via two new transfer learning mechanisms (Fast Fusion and Multi Fusion). We show that our method provides a flexible, state-of-the-art, architecture able to deal with noisy multimodal inputs.

null

10.18653/v1/2022.emnlp-main.582

null

27,712

inproceedings

li-etal-2022-transher

{T}ran{SHER}: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.583/

Li, Yizhi and Fan, Wei and Liu, Chao and Lin, Chenghua and Qian, Jiang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8517--8528

Knowledge graph embedding methods are important for the knowledge graph completion (or link prediction) task.One state-of-the-art method, PairRE, leverages two separate vectors to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion. To address this issue, we propose a novel score function TranSHER, which leverages relation-specific translations between head and tail entities to relax the constraint of hyper-ellipsoid restrictions. By introducing an intuitive and simple relation-specific translation, TranSHER can provide more direct guidance on optimization and capture more semantic characteristics of entities with complex relations. Experimental results show that TranSHER achieves state-of-the-art performance on link prediction and generalizes well to datasets in different domains and scales. Our codes are public available athttps://github.com/yizhilll/TranSHER.

null

10.18653/v1/2022.emnlp-main.583

null

27,713

inproceedings

deng-etal-2022-irrgn

{IRRGN}: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.584/

Deng, Jingcheng and Dai, Hengwei and Guo, Xuewei and Ju, Yuanchen and Peng, Wei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8529--8541

The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limited and inflexible. In addition, few studies consider differences between the options before and after reasoning. In this paper, we propose an Implicit Relational Reasoning Graph Network to address these issues, which consists of the Utterance Relational Reasoner (URR) and the Option Dual Comparator (ODC). URR aims to implicitly extract dependencies between utterances, as well as utterances and options, and make reasoning with relational graph convolutional networks. ODC focuses on perceiving the difference between the options through dual comparison, which can eliminate the interference of the noise options. Experimental results on two multi-turn dialogue reasoning benchmark datasets MuTual and MuTualplus show that our method significantly improves the baseline of four pre-trained language models and achieves state-of-the-art performance. The model surpasses human performance for the first time on the MuTual dataset.

null

10.18653/v1/2022.emnlp-main.584

null

27,714

inproceedings

zhu-zamani-2022-predicting

Predicting Prerequisite Relations for Unseen Concepts

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.585/

Zhu, Yaxin and Zamani, Hamed

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8542--8548

Concept prerequisite learning (CPL) plays a key role in developing technologies that assist people to learn a new complex topic or concept. Previous work commonly assumes that all concepts are given at training time and solely focuses on predicting the unseen prerequisite relationships between them. However, many real-world scenarios deal with concepts that are left undiscovered at training time, which is relatively unexplored. This paper studies this problem and proposes a novel alternating knowledge distillation approach to take advantage of both content- and graph-based models for this task. Extensive experiments on three public benchmarks demonstrate up to 10{\%} improvements in terms of F1 score.

null

10.18653/v1/2022.emnlp-main.585

null

27,715

inproceedings

chen-etal-2022-contrastive

Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.586/

Chen, Keqin and Zhang, Richong and Mensah, Samuel and Mao, Yongyi

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8549--8559

Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in a corresponding image using only caption-image annotations, i.e., without phrase-object annotations. Previous methods typically use a caption-image contrastive loss to indirectly supervise the alignment between phrases and objects, which hinders the maximum use of the intrinsic structure of the multimodal data and leads to unsatisfactory performance. In this work, we directly use the phrase-object contrastive loss in the condition that no positive annotation is available in the first place. Specifically, we propose a novel contrastive learning framework based on the expectation-maximization algorithm that adaptively refines the target prediction. Experiments on two widely used benchmarks, Flickr30K Entities and RefCOCO+, demonstrate the effectiveness of our framework. We obtain 63.05{\%} top-1 accuracy on Flickr30K Entities and 59.51{\%}/43.46{\%} on RefCOCO+ TestA/TestB, outperforming the previous methods by a large margin, even surpassing a previous SoTA that uses a pre-trained vision-language model. Furthermore, we deliver a theoretical analysis of the effectiveness of our method from the perspective of the maximum likelihood estimate with latent variables.

null

10.18653/v1/2022.emnlp-main.586

null

27,716

inproceedings

fei-etal-2022-beyond

Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.587/

Fei, Yu and Meng, Zhao and Nie, Ping and Wattenhofer, Roger and Sachan, Mrinmaya

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8560--8579

Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering their application to new situations. In this work, we show that zero-shot text classification can be improved simply by clustering texts in the embedding spaces of PLMs. Specifically, we fit the unlabeled texts with a Bayesian Gaussian Mixture Model after initializing cluster positions and shapes using class names. Despite its simplicity, this approach achieves superior or comparable performance on both topic and sentiment classification datasets and outperforms prior works significantly on unbalanced datasets. We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes. Our approach achieves an average of 20{\%} absolute improvement over prompt-based zero-shot learning. Finally, we compare different PLM embedding spaces and find that texts are well-clustered by topics even if the PLM is not explicitly pre-trained to generate meaningful sentence embeddings. This work indicates that PLM embeddings can categorize texts without task-specific fine-tuning, thus providing a new way to analyze and utilize their knowledge and zero-shot learning ability.

null

10.18653/v1/2022.emnlp-main.587

null

27,717

inproceedings

portelli-etal-2022-generalizing

Generalizing over Long Tail Concepts for Medical Term Normalization

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.588/

Portelli, Beatrice and Scaboro, Simone and Santus, Enrico and Sedghamiz, Hooman and Chersoni, Emmanuele and Serra, Giuseppe

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8580--8591

Medical term normalization consists in mapping a piece of text to a large number of output classes.Given the small size of the annotated datasets and the extremely long tail distribution of the concepts, it is of utmost importance to develop models that are capable to generalize to scarce or unseen concepts.An important attribute of most target ontologies is their hierarchical structure. In this paper we introduce a simple and effective learning strategy that leverages such information to enhance the generalizability of both discriminative and generative models.The evaluation shows that the proposed strategy produces state-of-the-art performance on seen concepts and consistent improvements on unseen ones, allowing also for efficient zero-shot knowledge transfer across text typologies and datasets.

null

10.18653/v1/2022.emnlp-main.588

null

27,718

inproceedings

song-etal-2022-unsupervised

Unsupervised Opinion Summarisation in the {W}asserstein Space

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.589/

Song, Jiayu and Bilal, Iman Munire and Tsakalidis, Adam and Procter, Rob and Liakata, Maria

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8592--8607

Opinion summarisation synthesises opinions expressed in a group of documents discussingthe same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus on opinion summarisation. To address these challenges we present WassOS, an unsupervised abstractive summarization model which makesuse of the Wasserstein distance. A Variational Autoencoder is first used to obtain the distribution of documents/posts, and the summary distribution is obtained as the Wasserstein barycenter. We create separate disentangled latent semantic and syntactic representations of the summary, which are fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments onmultiple datasets including reviews, Twitter clusters and Reddit threads show that WassOSalmost always outperforms the state-of-the-art on ROUGE metrics and consistently producesthe best summaries with respect to meaning preservation according to human evaluations.

null

10.18653/v1/2022.emnlp-main.589

null

27,719

inproceedings

leong-etal-2022-bloom

Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.590/

Leong, Colin and Nemecek, Joshua and Mansdorfer, Jacob and Filighera, Anna and Owodunni, Abraham and Whitenack, Daniel

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8608--8621

We present Bloom Library, a linguistically diverse set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition. These datasets represent either the most, or among the most, multilingual datasets for each of the included downstream tasks. In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families. We train downstream task models for various languages represented in the data, showing the viability of the data for future work in low-resource, multimodal NLP and establishing the first known baselines for these downstream tasks in certain languages (e.g., Bisu [bzi], with an estimated population of 700 users). Some of these first-of-their-kind baselines are comparable to state-of-the-art performance for higher-resourced languages. The Bloom Library datasets are released under Creative Commons licenses on the Hugging Face datasets hub to catalyze more linguistically diverse research in the included downstream tasks.

null

10.18653/v1/2022.emnlp-main.590

null

27,720

inproceedings

zerva-etal-2022-disentangling

Disentangling Uncertainty in Machine Translation Evaluation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.591/

Zerva, Chrysoula and Glushkova, Taisiya and Rei, Ricardo and Martins, Andr{\'e} F. T.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8622--8641

Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data. Recent work has attempted to mitigate this with simple uncertainty quantification techniques (Monte Carlo dropout and deep ensembles), however these techniques (as we show) are limited in several ways {--} for example, they are unable to distinguish between different kinds of uncertainty, and they are time and memory consuming. In this paper, we propose more powerful and efficient uncertainty predictors for MT evaluation, and we assess their ability to target different sources of aleatoric and epistemic uncertainty. To this end, we develop and compare training objectives for the COMET metric to enhance it with an uncertainty prediction output, including heteroscedastic regression, divergence minimization, and direct uncertainty prediction.Our experiments show improved results on uncertainty prediction for the WMT metrics task datasets, with a substantial reduction in computational costs. Moreover, they demonstrate the ability of these predictors to address specific uncertainty causes in MT evaluation, such as low quality references and out-of-domain data.

null

10.18653/v1/2022.emnlp-main.591

null

27,721

inproceedings

xu-etal-2022-model

Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.592/

Xu, Nan and Wang, Fei and Li, Bangzheng and Dong, Mingtao and Chen, Muhao

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8642--8658

Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations. To comprehensively investigate the faithfulness and reliability of entity typing methods, we first systematically define distinct kinds of model biases that are reflected mainly from spurious correlations. Particularly, we identify six types of existing model biases, including mention-context bias, lexical overlapping bias, named entity bias, pronoun bias, dependency bias, and overgeneralization bias. To mitigate model biases, we then introduce a counterfactual data augmentation method. By augmenting the original training set with their debiasedcounterparts, models are forced to fully comprehend sentences and discover the fundamental cues for entity typing, rather than relying on spurious correlations for shortcuts. Experimental results on the UFET dataset show our counterfactual data augmentation approach helps improve generalization of different entity typing models with consistently better performance on both the original and debiased test sets.

null

10.18653/v1/2022.emnlp-main.592

null

27,722

inproceedings

kassner-etal-2022-edin

{EDIN}: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.593/

Kassner, Nora and Petroni, Fabio and Plekhanov, Mikhail and Riedel, Sebastian and Cancedda, Nicola

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8659--8673

Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and because novel concepts arise constantly. We introduce the temporally segmented Unknown Entity Discovery and Indexing (EDIN)-benchmark where unknown entities, that is entities not part of the knowledge base and without descriptions and labeled mentions, have to be integrated into an existing entity linking system. By contrasting EDIN with zero-shot entity linking, we provide insight on the additional challenges it poses. Building on dense-retrieval based entity linking, we introduce the end-to-end EDIN-pipeline that detects, clusters, and indexes mentions of unknown entities in context. Experiments show that indexing a single embedding per entity unifying the information of multiple mentions works better than indexing mentions independently.

null

10.18653/v1/2022.emnlp-main.593

null

27,723

inproceedings

vallurupalli-etal-2022-poque

{POQ}ue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.594/

Vallurupalli, Sai and Ghosh, Sayontan and Erk, Katrin and Balasubramanian, Niranjan and Ferraro, Francis

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8674--8697

Knowledge about outcomes is critical for complex event understanding but is hard to acquire.We show that by pre-identifying a participant in a complex event, crowdworkers are ableto (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground theoutcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96weighted Fleiss Kappa). Our dataset, POQUe (Participant Outcome Questions), enables theexploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant`s influence over the event culmination.

null

10.18653/v1/2022.emnlp-main.594

null

27,724

inproceedings

ferrando-etal-2022-measuring

Measuring the Mixing of Contextual Information in the Transformer

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.595/

Ferrando, Javier and G{\'a}llego, Gerard I. and Costa-juss{\`a}, Marta R.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8698--8714

The Transformer architecture aggregates input information through the self-attention mechanism, but there is no clear understanding of how this information is mixed across the entire model. Additionally, recent works have demonstrated that attention weights alone are not enough to describe the flow of information. In this paper, we consider the whole attention block {--}multi-head attention, residual connection, and layer normalization{--} and define a metric to measure token-to-token interactions within each layer. Then, we aggregate layer-wise interpretations to provide input attribution scores for model predictions. Experimentally, we show that our method, ALTI (Aggregation of Layer-wise Token-to-token Interactions), provides more faithful explanations and increased robustness than gradient-based methods.

null

10.18653/v1/2022.emnlp-main.595

null

27,725

inproceedings

daza-etal-2022-dealing

Dealing with Abbreviations in the {S}lovenian Biographical Lexicon

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.596/

Daza, Angel and Fokkens, Antske and Erjavec, Toma{\v{z}}

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8715--8720

Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.

null

10.18653/v1/2022.emnlp-main.596

null

27,726

inproceedings

ogundepo-etal-2022-africlirmatrix

{A}fri{CLIRM}atrix: Enabling Cross-Lingual Information Retrieval for {A}frican Languages

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.597/

Ogundepo, Odunayo and Zhang, Xinyu and Sun, Shuo and Duh, Kevin and Lin, Jimmy

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8721--8728

Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers' ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse{--}dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix.

null

10.18653/v1/2022.emnlp-main.597

null

27,727

inproceedings

ravichander-etal-2022-condaqa

{CONDAQA}: A Contrastive Reading Comprehension Dataset for Reasoning about Negation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.598/

Ravichander, Abhilasha and Gardner, Matt and Marasovic, Ana

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8729--8755

The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for current natural language understanding systems. To facilitate the future development of models that can process negation effectively, we present CONDAQA, the first English reading comprehension dataset which requires reasoning about the implications of negated statements in paragraphs. We collect paragraphs with diverse negation cues, then have crowdworkers ask questions about the implications of the negated statement in the passage. We also have workers make three kinds of edits to the passage{---}paraphrasing the negated statement, changing the scope of the negation, and reversing the negation{---}resulting in clusters of question-answer pairs that are difficult for models to answer with spurious shortcuts. CONDAQA features 14,182 question-answer pairs with over 200 unique negation cues and is challenging for current state-of-the-art models. The best performing model on CONDAQA (UnifiedQA-v2-3b) achieves only 42{\%} on our consistency metric, well below human performance which is 81{\%}. We release our dataset, along with fully-finetuned, few-shot, and zero-shot evaluations, to facilitate the development of future NLP methods that work on negated language.

null

10.18653/v1/2022.emnlp-main.598

null

27,728

inproceedings

ferrando-etal-2022-towards

Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.599/

Ferrando, Javier and G{\'a}llego, Gerard I. and Alastruey, Belen and Escolano, Carlos and Costa-juss{\`a}, Marta R.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8756--8769

In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and the target prefix (what has been previously translated at a decoding step). However, previous work on interpretability in NMT has mainly focused solely on source sentence tokens' attributions. Therefore, we lack a full understanding of the influences of every input token (source sentence and target prefix) in the model predictions. In this work, we propose an interpretability method that tracks input tokens' attributions for both contexts. Our method, which can be extended to any encoder-decoder Transformer-based model, allows us to better comprehend the inner workings of current NMT models. We apply the proposed method to both bilingual and multilingual Transformers and present insights into their behaviour.

null

10.18653/v1/2022.emnlp-main.599

null

27,729

inproceedings

mohamed-etal-2022-artelingo

{A}rt{EL}ingo: A Million Emotion Annotations of {W}iki{A}rt with Emphasis on Diversity over Language and Culture

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.600/

Mohamed, Youssef and Abdelfattah, Mohamed and Alhuwaider, Shyma and Li, Feifan and Zhang, Xiangliang and Church, Kenneth and Elhoseiny, Mohamed

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8770--8785

This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, ArtELingo adds another 0.79M annotations in Arabic and Chinese, plus 4.8K in Spanish to evaluate {\textquotedblleft}cultural-transfer{\textquotedblright} performance. 51K artworks have 5 annotations or more in 3 languages. This diversity makes it possible to study similarities and differences across languages and cultures. Further, we investigate captioning tasks, and find diversity improves the performance of baseline models. ArtELingo is publicly available at {\textquoteleft}www.artelingo.org{\textquoteleft} with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.

null

10.18653/v1/2022.emnlp-main.600

null

27,730

inproceedings

adolphs-etal-2022-decoding

Decoding a Neural Retriever`s Latent Space for Query Suggestion

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.601/

Adolphs, Leonard and Chen Huebscher, Michelle and Buck, Christian and Girgin, Sertan and Bachem, Olivier and Ciaramita, Massimiliano and Hofmann, Thomas

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8786--8804

Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-words models; it is not trivial to connect a query change to a change in the latent space that ultimately determines the retrieval results. To shed light on this embedding space, we learn a {\textquotedblleft}query decoder{\textquotedblright} that, given a latent representation of a neural search engine, generates the corresponding query. We show that it is possible to decode a meaningful query from its latent representation and, when moving in the right direction in latent space, to decode a query that retrieves the relevant paragraph. In particular, the query decoder can be useful to understand {\textquotedblleft}what should have been asked{\textquotedblright} to retrieve a particular paragraph from the collection. We employ the query decoder to generate a large synthetic dataset of query reformulations for MSMarco, leading to improved retrieval performance. On this data, we train a pseudo-relevance feedback (PRF) T5 model for the application of query suggestion that outperforms both query reformulation and PRF information retrieval baselines.

null

10.18653/v1/2022.emnlp-main.601

null

27,731

inproceedings

jangra-etal-2022-star

{T}-{STAR}: Truthful Style Transfer using {AMR} Graph as Intermediate Representation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.602/

Jangra, Anubhav and Nema, Preksha and Raghuveer, Aravindan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8805--8825

Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed that is devoid of style while still preserving the meaning of the source sentence. In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation. We posit that semantic notations like AMR are a natural choice for an intermediate representation. Hence, we propose T-STAR: a model comprising of two components, text-to-AMR encoder and a AMR-to-text decoder. We propose several modeling improvements to enhance the style agnosticity of the generated AMR. To the best of our knowledge, T-STAR is the first work that uses AMR as an intermediate representation for TST. With thorough experimental evaluation we show T-STAR significantly outperforms state of the art techniques by achieving on an average 15.2{\%} higher content preservation with negligible loss ({\textasciitilde}3{\%}) in style accuracy. Through detailed human evaluation with 90,000 ratings, we also show that T-STAR has upto 50{\%} lesser hallucinations compared to state of the art TST models.

null

10.18653/v1/2022.emnlp-main.602

null

27,732

inproceedings

jiang-etal-2022-promptbert

{P}rompt{BERT}: Improving {BERT} Sentence Embeddings with Prompts

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.603/

Jiang, Ting and Jiao, Jian and Huang, Shaohan and Zhang, Zihan and Wang, Deqing and Zhuang, Fuzhen and Wei, Furu and Huang, Haizhen and Deng, Denvy and Zhang, Qi

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8826--8837

We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analysis the drawback of current sentence embedding from original BERT and find that it is mainly due to the static token embedding bias and ineffective BERT layers. Then we propose the first prompt-based sentence embeddings method and discuss two prompt representing methods and three prompt searching methods to make BERT achieve better sentence embeddings .Moreover, we propose a novel unsupervised training objective by the technology of template denoising, which substantially shortens the performance gap between the supervised and unsupervised settings. Extensive experiments show the effectiveness of our method. Compared to SimCSE, PromptBert achieves 2.29 and 2.58 points of improvement based on BERT and RoBERTa in the unsupervised setting.

null

10.18653/v1/2022.emnlp-main.603

null

27,733

inproceedings

jain-etal-2022-extending

Extending Logic Explained Networks to Text Classification

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.604/

Jain, Rishabh and Ciravegna, Gabriele and Barbiero, Pietro and Giannini, Francesco and Buffelli, Davide and Lio, Pietro

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8838--8857

Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions.However, these models have only been applied to vision and tabular data, and they mostly favour the generation of global explanations, while local ones tend to be noisy and verbose.For these reasons, we propose LEN{\ensuremath{<}}sup{\ensuremath{>}}p{\ensuremath{<}}/sup{\ensuremath{>}}, improving local explanations by perturbing input words, and we test it on text classification. Our results show that (i) LEN{\ensuremath{<}}sup{\ensuremath{>}}p{\ensuremath{<}}/sup{\ensuremath{>}} provides better local explanations than LIME in terms of sensitivity and faithfulness, and (ii) its logic explanations are more useful and user-friendly than the feature scoring provided by LIME as attested by a human survey.

null

10.18653/v1/2022.emnlp-main.604

null

27,734

inproceedings

liu-etal-2022-uni

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.605/

Liu, Ye and Yavuz, Semih and Meng, Rui and Radev, Dragomir and Xiong, Caiming and Zhou, Yingbo

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8858--8869

Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB). However, existing approaches on semantic parsing cannot adapt to both modalities, as they suffer from the exponential growth of the logical form candidates and can hardly generalize to unseen data.In this work, we propose Uni-Parser, a unified semantic parser for question answering (QA) on both KB and DB. We define the primitive (relation and entity in KB, and table name, column name and cell value in DB) as the essential element in our framework. The number of primitives grows only at a linear rate to the number of retrieved relations in KB and DB, preventing us from exponential logic form candidates. We leverage the generator to predict final logical forms by altering and composing top-ranked primitives with different operations (e.g. select, where, count). With sufficiently pruned search space by a contrastive primitive ranker, the generator is empowered to capture the composition of primitives enhancing its generalization ability. We achieve competitive results on multiple KB and DB QA benchmarks with more efficiency, especially in the compositional and zero-shot settings.

null

10.18653/v1/2022.emnlp-main.605

null

27,735

inproceedings

tian-etal-2022-rapo

{RAPO}: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.606/

Tian, Zhoujin and Li, Chaozhuo and Ren, Shuo and Zuo, Zhiqiang and Wen, Zengxuan and Hu, Xinyue and Han, Xiao and Huang, Haizhen and Deng, Denvy and Zhang, Qi and Xie, Xing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8870--8883

Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in \url{https://github.com/Jlfj345wf/RAPO}.

null

10.18653/v1/2022.emnlp-main.606

null

27,736

inproceedings

amini-cotterell-2022-parsing

On Parsing as Tagging

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.607/

Amini, Afra and Cotterell, Ryan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8884--8900

There are many proposals to reduce constituency parsing to tagging. To figure out what these approaches have in common, we offer a unifying pipeline, which consists of three steps: linearization, learning, and decoding. We prove that classic shift{--}reduce parsing can be reduced to tetratagging{---}the state-of-the-art constituency tagger{---}under two assumptions: right-corner transformation in the linearization step and factored scoring in the learning step. We ask what is the most critical factor that makes parsing-as-tagging methods accurate while being efficient. To answer this question, we empirically evaluate a taxonomy of tagging pipelines with different choices of linearizers, learners, and decoders. Based on the results in English as well as a set of 8 typologically diverse languages, we conclude that the linearization of the derivation tree and its alignment with the input sequence is the most critical factor in achieving accurate parsers as taggers.

null

10.18653/v1/2022.emnlp-main.607

null

27,737

inproceedings

wang-etal-2022-distilled

Distilled Dual-Encoder Model for Vision-Language Understanding

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.608/

Wang, Zekun and Wang, Wenhui and Zhu, Haichao and Liu, Ming and Qin, Bing and Wei, Furu

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8901--8913

On vision-language understanding (VLU) tasks, fusion-encoder vision-language models achieve superior results but sacrifice efficiency because of the simultaneous encoding of images and text. On the contrary, the dual encoder model that separately encodes images and text has the advantage in efficiency, while failing on VLU tasks due to the lack of deep cross-modal interactions. To get the best of both worlds, we propose DiDE, a framework that distills the knowledge of the fusion-encoder teacher model into the dual-encoder student model. Since the cross-modal interaction is the key to the superior performance of teacher model but is absent in the student model, we encourage the student not only to mimic the predictions of teacher, but also to calculate the cross-modal attention distributions and align with the teacher. Experimental results demonstrate that DiDE is competitive with the fusion-encoder teacher model in performance (only a 1{\%} drop) while enjoying 4 times faster inference. Further analyses reveal that the proposed cross-modal attention distillation is crucial to the success of our framework.

null

10.18653/v1/2022.emnlp-main.608

null

27,738

inproceedings

chen-etal-2022-argument

Argument Mining for Review Helpfulness Prediction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.609/

Chen, Zaiqian and Verdi do Amarante, Daniel and Donaldson, Jenna and Jo, Yohan and Park, Joonsuk

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8914--8922

The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites. And argumentational features{---}such as the structure of arguments and the types of underlying elementary units{---}have shown to be promising indicators of product review helpfulness. However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility. To this end, we present the AMazon Argument Mining (AM$^2$) corpus{---}a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.

null

10.18653/v1/2022.emnlp-main.609

null

27,739

inproceedings

sadat-caragea-2022-hierarchical

Hierarchical Multi-Label Classification of Scientific Documents

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.610/

Sadat, Mobashir and Caragea, Cornelia

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8923--8937

Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection. With the large number of topics being available in recent years, it has become necessary to arrange them in a hierarchy. Therefore, the automatic classification systems need to be able to classify the documents hierarchically. In addition, each paper is often assigned to more than one relevant topic. For example, a paper can be assigned to several topics in a hierarchy tree. In this paper, we introduce a new dataset for hierarchical multi-label text classification (HMLTC) of scientific papers called SciHTC, which contains 186,160 papers and 1,234 categories from the ACM CCS tree. We establish strong baselines for HMLTC and propose a multi-task learning approach for topic classification with keyword labeling as an auxiliary task. Our best model achieves a Macro-F1 score of 34.57{\%} which shows that this dataset provides significant research opportunities on hierarchical scientific topic classification. We make our dataset and code for all experiments publicly available.

null

10.18653/v1/2022.emnlp-main.610

null

27,740

inproceedings

liu-etal-2022-rainier

Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.611/

Liu, Jiacheng and Hallinan, Skyler and Lu, Ximing and He, Pengfei and Welleck, Sean and Hajishirzi, Hannaneh and Choi, Yejin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8938--8958

Knowledge underpins reasoning. Recent research demonstrates that when relevant knowledge is provided as additional context to commonsense question answering (QA), it can substantially enhance the performance even on top of state-of-the-art. The fundamental challenge is where and how to find such knowledge that is high quality and on point with respect to the question; knowledge retrieved from knowledge bases are incomplete and knowledge generated from language models are inconsistent.We present Rainier, or Reinforced Knowledge Introspector, that learns to generate contextually relevant knowledge in response to given questions. Our approach starts by imitating knowledge generated by GPT-3, then learns to generate its own knowledge via reinforcement learning where rewards are shaped based on the increased performance on the resulting question answering. Rainier demonstrates substantial and consistent performance gains when tested over 9 different commonsense benchmarks: including 5 datasets that are seen during model training, as well as 4 datasets that are kept unseen. Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.

null

10.18653/v1/2022.emnlp-main.611

null

27,741

inproceedings

kann-etal-2022-major

A Major Obstacle for {NLP} Research: Let`s Talk about Time Allocation!

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.612/

Kann, Katharina and Dudy, Shiran and McCarthy, Arya D.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8959--8969

The field of natural language processing (NLP) has grown over the last few years: conferences have become larger, we have published an incredible amount of papers, and state-of-the-art research has been implemented in a large variety of customer-facing products. However, this paper argues that we have been less successful than we *should* have been and reflects on where and how the field fails to tap its full potential. Specifically, we demonstrate that, in recent years, **subpar time allocation has been a major obstacle for NLP research**. We outline multiple concrete problems together with their negative consequences and, importantly, suggest remedies to improve the status quo. We hope that this paper will be a starting point for discussions around which common practices are {--} or are *not* {--} beneficial for NLP research.

null

10.18653/v1/2022.emnlp-main.612

null

27,742

inproceedings

vijjini-etal-2022-towards

Towards Inter-character Relationship-driven Story Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.613/

Vijjini, Anvesh Rao and Brahman, Faeze and Chaturvedi, Snigdha

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8970--8987

In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference brings interpretability to ReLiSt.

null

10.18653/v1/2022.emnlp-main.613

null

27,743

inproceedings

baumgartner-etal-2022-incorporating

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.614/

Baumg{\"artner, Tim and Ribeiro, Leonardo F. R. and Reimers, Nils and Gurevych, Iryna

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8988--9005

Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like question answering or navigational queries, however, for information-seeking scenarios, users often provide information on whether a document is relevant to their query in form of clicks or explicit feedback. Therefore, in this work, we explore how relevance feedback can be directly integrated into neural re-ranking models by adopting few-shot and parameter-efficient learning techniques. Specifically, we introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. Further, we explore Cross-Encoder models that we pre-train using meta-learning and subsequently fine-tune for each query, training only on the feedback documents. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario. Extensive experiments demonstrate that integrating relevance feedback directly in neural re-ranking models improves their performance, and fusing lexical ranking with our best performing neural re-ranker outperforms all other methods by 5.2{\%} nDCG@20.

null

10.18653/v1/2022.emnlp-main.614

null

27,744

inproceedings

zhao-etal-2022-reastap

{R}eas{TAP}: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.615/

Zhao, Yilun and Nan, Linyong and Qi, Zhenting and Zhang, Rui and Radev, Dragomir

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9006--9018

Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction. Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates. We model the table pre-training task as a sequence generation task and pre-train ReasTAP to generate precise answers of the synthetic examples. ReasTAP is evaluated on four benchmarks covering three downstream tasks including 1) WikiSQL-Weak and WikiTQ for Table Question Answering, 2) TabFact for Table Fact Verification, and 3) LogicNLG for Faithful Table-to-Text Generation. Experimental results demonstrate that ReasTAP achieves new state-of-the-art results on all of them and delivers a significant improvement under low-resource setting. Our code is publicly available at https://github.com/Yale-LILY/ReasTAP.

null

10.18653/v1/2022.emnlp-main.615

null

27,745

inproceedings

lin-etal-2022-shot

Few-shot Learning with Multilingual Generative Language Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.616/

Lin, Xi Victoria and Mihaylov, Todor and Artetxe, Mikel and Wang, Tianlu and Chen, Shuohui and Simig, Daniel and Ott, Myle and Goyal, Naman and Bhosale, Shruti and Du, Jingfei and Pasunuru, Ramakanth and Shleifer, Sam and Koura, Punit Singh and Chaudhary, Vishrav and O{'}Horo, Brian and Wang, Jeff and Zettlemoyer, Luke and Kozareva, Zornitsa and Diab, Mona and Stoyanov, Veselin and Li, Xian

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9019--9052

Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual generative language models on a corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4{\%} absolute accuracy improvement in 0-shot settings and +9.4{\%} in 4-shot settings) and natural language inference (+5.4{\%} in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while surpassing the official supervised baseline in 45 directions. We conduct an in-depth analysis of different multilingual prompting approaches, showing in particular that strong few-shot learning performance across languages can be achieved via cross-lingual transfer through both templates and demonstration examples.

null

10.18653/v1/2022.emnlp-main.616

null

27,746

inproceedings

liu-neubig-2022-representations

Are representations built from the ground up? An empirical examination of local composition in language models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.617/

Liu, Emmy and Neubig, Graham

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9053--9073

Compositionality, the phenomenon where the meaning of a phrase can be derived from its constituent parts, is a hallmark of human language. At the same time, many phrases are non-compositional, carrying a meaning beyond that of each part in isolation. Representing both of these types of phrases is critical for language understanding, but it is an open question whether modern language models (LMs) learn to do so; in this work we examine this question. We first formulate a problem of predicting the LM-internal representations of longer phrases given those of their constituents. We find that the representation of a parent phrase can be predicted with some accuracy given an affine transformation of its children. While we would expect the predictive accuracy to correlate with human judgments of semantic compositionality, we find this is largely not the case, indicating that LMs may not accurately distinguish between compositional and non-compositional phrases. We perform a variety of analyses, shedding light on when different varieties of LMs do and do not generate compositional representations, and discuss implications for future modeling work.

null

10.18653/v1/2022.emnlp-main.617

null

27,747

inproceedings

chong-etal-2022-detecting

Detecting Label Errors by Using Pre-Trained Language Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.618/

Chong, Derek and Hong, Jenny and Manning, Christopher

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9074--9091

We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly outperforms more complex error-detection mechanisms proposed in previous work. To this end, we contribute a novel method for introducing realistic, human-originated label noise into existing crowdsourced datasets such as SNLI and TweetNLP. We show that this noise has similar properties to real, hand-verified label errors, and is harder to detect than existing synthetic noise, creating challenges for model robustness.We argue that human-originated noise is a better standard for evaluation than synthetic noise. Finally, we use crowdsourced verification to evaluate the detection of real errors on IMDB, Amazon Reviews, and Recon, and confirm that pre-trained models perform at a 9{--}36{\%} higher absolute Area Under the Precision-Recall Curve than existing models.

null

10.18653/v1/2022.emnlp-main.618

null

27,748

inproceedings

ogueji-etal-2022-intriguing

Intriguing Properties of Compression on Multilingual Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.619/

Ogueji, Kelechi and Ahia, Orevaoghene and Onilude, Gbemileke and Gehrmann, Sebastian and Hooker, Sara and Kreutzer, Julia

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9092--9110

Multilingual models are often particularly dependent on scaling to generalize to a growing number of languages. Compression techniques are widely relied upon to reconcile the growth in model size with real world resource constraints, but compression can have a disparate effect on model performance for low-resource languages. It is thus crucial to understand the trade-offs between scale, multilingualism, and compression. In this work, we propose an experimental framework to characterize the impact of sparsifying multilingual pre-trained language models during fine-tuning.Applying this framework to mBERT named entity recognition models across 40 languages, we find that compression confers several intriguing and previously unknown generalization properties. In contrast to prior findings, we find that compression may improve model robustness over dense models. We additionally observe that under certain sparsification regimes compression may aid, rather than disproportionately impact the performance of low-resource languages.

null

10.18653/v1/2022.emnlp-main.619

null

27,749

inproceedings

born-etal-2022-sequence

Sequence Models for Document Structure Identification in an Undeciphered Script

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.620/

Born, Logan and Monroe, M. and Kelley, Kathryn and Sarkar, Anoop

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9111--9121

This work describes the first thorough analysis of {\textquotedblleft}header{\textquotedblright} signs in proto-Elamite, an undeciphered script from 3100-2900 BCE. Headers are a category of signs which have been provisionally identified through painstaking manual analysis of this script by domain experts. We use unsupervised neural and statistical sequence modeling techniques to provide new and independent evidence for the existence of headers, without supervision from domain experts. Having affirmed the existence of headers as a legitimate structural feature, we next arrive at a richer understanding of their possible meaning and purpose by (i) examining which features predict their presence; (ii) identifying correlations between these features and other document properties; and (iii) examining cases where these features predict the presence of a header in texts where domain experts do not expect one (or vice versa). We provide more concrete processes for labeling headers in this corpus and a clearer justification for existing intuitions about document structure in proto-Elamite.

null

10.18653/v1/2022.emnlp-main.620

null

27,750

inproceedings

wang-etal-2022-english

{E}nglish Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.621/

Wang, Yaushian and Wu, Ashley and Neubig, Graham

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9122--9133

Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data.In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS.The performance can be further enhanced when cross-lingual NLI data is available.

null

10.18653/v1/2022.emnlp-main.621

null

27,751

inproceedings

zhang-etal-2022-active

Active Example Selection for In-Context Learning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.622/

Zhang, Yiming and Feng, Shi and Tan, Chenhao

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9134--9148

With a handful of demonstration examples, large-scale language models demonstrate strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a 5.8{\%} improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.

null

10.18653/v1/2022.emnlp-main.622

null

27,752

inproceedings

fabbri-etal-2022-improving

Improving Factual Consistency in Summarization with Compression-Based Post-Editing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.623/

Fabbri, Alex and Choubey, Prafulla Kumar and Vig, Jesse and Wu, Chien-Sheng and Xiong, Caiming

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9149--9156

State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary`s essential information and form. We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed. We show that this model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30{\%} on XSum, and that this model can be applied on top of another post-editor, improving entity precision by up to a total of 38{\%}. We perform an extensive comparison of post-editing approaches that demonstrate trade-offs between factual consistency, informativeness, and grammaticality, and we analyze settings where post-editors show the largest improvements.

null

10.18653/v1/2022.emnlp-main.623

null

27,753

inproceedings

qiu-etal-2022-evaluating

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.624/

Qiu, Linlu and Shaw, Peter and Pasupat, Panupong and Shi, Tianze and Herzig, Jonathan and Pitler, Emily and Sha, Fei and Toutanova, Kristina

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9157--9179

Despite their strong performance on many tasks, pre-trained language models have been shown to struggle on out-of-distribution compositional generalization. Meanwhile, recent work has shown considerable improvements on many NLP tasks from model scaling. Can scaling up model size also improve compositional generalization in semantic parsing? We evaluate encoder-decoder models up to 11B parameters and decoder-only models up to 540B parameters, and compare model scaling curves for three different methods for applying a pre-trained language model to a new task: fine-tuning all parameters, prompt tuning, and in-context learning. We observe that fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional generalization in semantic parsing evaluations. In-context learning has positive scaling curves, but is generally outperformed by much smaller fine-tuned models. Prompt-tuning can outperform fine-tuning, suggesting further potential improvements from scaling as it exhibits a more positive scaling curve. Additionally, we identify several error trends that vary with model scale. For example, larger models are generally better at modeling the syntax of the output space, but are also more prone to certain types of overfitting. Overall, our study highlights limitations of current techniques for effectively leveraging model scale for compositional generalization, while our analysis also suggests promising directions for future work.

null

10.18653/v1/2022.emnlp-main.624

null

27,754

inproceedings

smith-etal-2022-im

{\textquotedblleft}{I}`m sorry to hear that{\textquotedblright}: Finding New Biases in Language Models with a Holistic Descriptor Dataset

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.625/

Smith, Eric Michael and Hall, Melissa and Kambadur, Melanie and Presani, Eleonora and Williams, Adina

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9180--9211

As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presuppose which types of biases models can exhibit. In this work, we present a new, more inclusive bias measurement dataset, HolisticBias, which includes nearly 600 descriptor terms across 13 different demographic axes. HolisticBias was assembled in a participatory process including experts and community members with lived experience of these terms. These descriptors combine with a set of bias measurement templates to produce over 450,000 unique sentence prompts, which we use to explore, identify, and reduce novel forms of bias in several generative models. We demonstrate that HolisticBias is effective at measuring previously undetectable biases in token likelihoods from language models, as well as in an offensiveness classifier. We will invite additions and amendments to the dataset, which we hope will serve as a basis for more easy-to-use and standardized methods for evaluating bias in NLP models.

null

10.18653/v1/2022.emnlp-main.625

null

27,755

inproceedings

wang-etal-2022-understanding-multimodal

Understanding {ME}? Multimodal Evaluation for Fine-grained Visual Commonsense

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.626/

Wang, Zhecan and You, Haoxuan and He, Yicheng and Li, Wenhao and Chang, Kai-Wei and Chang, Shih-Fu

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9212--9224

Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve comprehension of the visual scene described. Recently, various approaches have been developed and have achieved high performance on visual commonsense benchmarks. However, it is unclear whether the models really understand the visual scene and underlying commonsense knowledge due to limited evaluation data resources. To provide an in-depth analysis, we present a Multimodal Evaluation (ME) pipeline to automatically generate question-answer pairs to test models' understanding of the visual scene, text, and related knowledge. We then take a step further to show that training with the ME data boosts the model`s performance in standard VCR evaluation. Lastly, our in-depth analysis and comparison reveal interesting findings: (1) semantically low-level information can assist the learning of high-level information but not the opposite; (2) visual information is generally under utilization compared with text.

null

10.18653/v1/2022.emnlp-main.626

null

27,756

inproceedings

ma-etal-2022-semantic

Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.627/

Ma, Nianzu and Mazumder, Sahisnu and Politowicz, Alexander and Liu, Bing and Robertson, Eric and Grigsby, Scott

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9225--9252

Much of the existing work on text novelty detection has been studied at the topic level, i.e., identifying whether the topic of a document or a sentence is novel or not. Little work has been done at the fine-grained semantic level (or contextual level). For example, given that we know Elon Musk is the CEO of a technology company, the sentence {\textquotedblleft}Elon Musk acted in the sitcom The Big Bang Theory{\textquotedblright} is novel and surprising because normally a CEO would not be an actor. Existing topic-based novelty detection methods work poorly on this problem because they do not perform semantic reasoning involving relations between named entities in the text and their background knowledge. This paper proposes an effective model (called PAT-SND) to solve the problem, which can also characterize the novelty. An annotated dataset is also created. Evaluation shows that PAT-SND outperforms 10 baselines by large margins.

null

10.18653/v1/2022.emnlp-main.627

null

27,757

inproceedings

wang-etal-2022-cn

{CN}-{A}uto{MIC}: Distilling {C}hinese Commonsense Knowledge from Pretrained Language Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.628/

Wang, Chenhao and Li, Jiachun and Chen, Yubo and Liu, Kang and Zhao, Jun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9253--9265

Commonsense knowledge graphs (CKGs) are increasingly applied in various natural language processing tasks. However, most existing CKGs are limited to English, which hinders related research in non-English languages. Meanwhile, directly generating commonsense knowledge from pretrained language models has recently received attention, yet it has not been explored in non-English languages. In this paper, we propose a large-scale Chinese CKG generated from multilingual PLMs, named as **CN-AutoMIC**, aiming to fill the research gap of non-English CKGs. To improve the efficiency, we propose generate-by-category strategy to reduce invalid generation. To ensure the filtering quality, we develop cascaded filters to discard low-quality results. To further increase the diversity and density, we introduce a bootstrapping iteration process to reuse generated results. Finally, we conduct detailed analyses on CN-AutoMIC from different aspects. Empirical results show the proposed CKG has high quality and diversity, surpassing the direct translation version of similar English CKGs. We also find some interesting deficiency patterns and differences between relations, which reveal pending problems in commonsense knowledge generation. We share the resources and related models for further study.

null

10.18653/v1/2022.emnlp-main.628

null

27,758

inproceedings

hosseini-caragea-2022-calibrating

Calibrating Student Models for Emotion-related Tasks

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.629/

Hosseini, Mahshid and Caragea, Cornelia

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9266--9278

Knowledge Distillation (KD) is an effective method to transfer knowledge from one network (a.k.a. teacher) to another (a.k.a. student). In this paper, we study KD on the emotion-related tasks from a new perspective: calibration. We further explore the impact of the mixup data augmentation technique on the distillation objective and propose to use a simple yet effective mixup method informed by training dynamics for calibrating the student models. Underpinned by the regularization impact of the mixup process by providing better training signals to the student models using training dynamics, our proposed mixup strategy gradually enhances the student model`s calibration while effectively improving its performance. We evaluate the calibration of pre-trained language models through knowledge distillation over three tasks of emotion detection, sentiment analysis, and empathy detection. By conducting extensive experiments on different datasets, with both in-domain and out-of-domain test sets, we demonstrate that student models distilled from teacher models trained using our proposed mixup method obtained the lowest Expected Calibration Errors (ECEs) and best performance on both in-domain and out-of-domain test sets.

null

10.18653/v1/2022.emnlp-main.629

null

27,759

inproceedings

vu-etal-2022-overcoming

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.630/

Vu, Tu and Barua, Aditya and Lester, Brian and Cer, Daniel and Iyyer, Mohit and Constant, Noah

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9279--9300

In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study. We assume a strict setting with no access to parallel data or machine translation and find that common transfer learning approaches struggle in this setting, as a generative multilingual model fine-tuned purely on English catastrophically forgets how to generate non-English. Given the recent rise of parameter-efficient adaptation techniques, we conduct the first investigation into how one such method, prompt tuning (Lester et al., 2021), can overcome catastrophic forgetting to enable zero-shot cross-lingual generation. Our experiments show that parameter-efficient prompt tuning provides gains over standard fine-tuning when transferring between less-related languages, e.g., from English to Thai. However, a significant gap still remains between these methods and fully-supervised baselines. To improve cross-lingual transfer further, we explore several approaches, including: (1) mixing in unlabeled multilingual data, and (2) explicitly factoring prompts into recombinable language and task components. Our approaches can provide further quality gains, suggesting that robust zero-shot cross-lingual generation is within reach.

null

10.18653/v1/2022.emnlp-main.630

null

27,760

inproceedings

dou-etal-2022-improving

Improving Large-scale Paraphrase Acquisition and Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.631/

Dou, Yao and Jiang, Chao and Xu, Wei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9301--9323

This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks. We present a new Multi-Topic Paraphrase in Twitter (MultiPIT) corpus that consists of a total of 130k sentence pairs with crowdsoursing (MultiPIT{\_}crowd) and expert (MultiPIT{\_}expert) annotations using two different paraphrase definitions for paraphrase identification, in addition to a multi-reference test set (MultiPIT{\_}NMR) and a large automatically constructed training set (MultiPIT{\_}Auto) for paraphrase generation. With improved data annotation quality and task-specific paraphrase definition, the best pre-trained language model fine-tuned on our dataset achieves the state-of-the-art performance of 84.2 F1 for automatic paraphrase identification. Furthermore, our empirical results also demonstrate that the paraphrase generation models trained on MultiPIT{\_}Auto generate more diverse and high-quality paraphrases compared to their counterparts fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT.

null

10.18653/v1/2022.emnlp-main.631

null

27,761

inproceedings

oh-schuler-2022-entropy

Entropy- and Distance-Based Predictors From {GPT}-2 Attention Patterns Predict Reading Times Over and Above {GPT}-2 Surprisal

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.632/

Oh, Byung-Doh and Schuler, William

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9324--9334

Transformer-based large language models are trained to make predictions about the next word by aggregating representations of previous tokens through their self-attention mechanism. In the field of cognitive modeling, such attention patterns have recently been interpreted as embodying the process of cue-based retrieval, in which attention over multiple targets is taken to generate interference and latency during retrieval. Under this framework, this work first defines an entropy-based predictor that quantifies the diffuseness of self-attention, as well as distance-based predictors that capture the incremental change in attention patterns across timesteps. Moreover, following recent studies that question the informativeness of attention weights, we also experiment with alternative methods for incorporating vector norms into attention weights. Regression experiments using predictors calculated from the GPT-2 language model show that these predictors deliver a substantially better fit to held-out self-paced reading and eye-tracking data over a rigorous baseline including GPT-2 surprisal.

null

10.18653/v1/2022.emnlp-main.632

null

27,762

inproceedings

ali-hassan-2022-survey

A Survey of Computational Framing Analysis Approaches

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.633/

Ali, Mohammad and Hassan, Naeemul

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9335--9348

Framing analysis is predominantly qualitative and quantitative, examining a small dataset with manual coding. Easy access to digital data in the last two decades prompts scholars in both computation and social sciences to utilize various computational methods to explore frames in large-scale datasets. The growing scholarship, however, lacks a comprehensive understanding and resources of computational framing analysis methods. Aiming to address the gap, this article surveys existing computational framing analysis approaches and puts them together. The research is expected to help scholars and journalists gain a deeper understanding of how frames are being explored computationally, better equip them to analyze frames in large-scale datasets, and, finally, work on advancing methodological approaches.

null

10.18653/v1/2022.emnlp-main.633

null

27,763

inproceedings

nguyen-etal-2022-learning

Learning Cross-Task Dependencies for Joint Extraction of Entities, Events, Event Arguments, and Relations

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.634/

Nguyen, Minh Van and Min, Bonan and Dernoncourt, Franck and Nguyen, Thien

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9349--9360

Extracting entities, events, event arguments, and relations (i.e., task instances) from text represents four main challenging tasks in information extraction (IE), which have been solved jointly (JointIE) to boost the overall performance for IE. As such, previous work often leverages two types of dependencies between the tasks, i.e., cross-instance and cross-type dependencies representing relatedness between task instances and correlations between information types of the tasks. However, the cross-task dependencies in prior work are not optimal as they are only designed manually according to some task heuristics. To address this issue, we propose a novel model for JointIE that aims to learn cross-task dependencies from data. In particular, we treat each task instance as a node in a dependency graph where edges between the instances are inferred through information from different layers of a pretrained language model (e.g., BERT). Furthermore, we utilize the Chow-Liu algorithm to learn a dependency tree between information types for JointIE by seeking to approximate the joint distribution of the types from data. Finally, the Chow-Liu dependency tree is used to generate cross-type patterns, serving as anchor knowledge to guide the learning of representations and dependencies between instances for JointIE. Experimental results show that our proposed model significantly outperforms strong JointIE baselines over four datasets with different languages.

null

10.18653/v1/2022.emnlp-main.634

null

27,764

inproceedings

min-etal-2022-dont

Don`t Copy the Teacher: Data and Model Challenges in Embodied Dialogue

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.635/

Min, So Yeon and Zhu, Hao and Salakhutdinov, Ruslan and Bisk, Yonatan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9361--9368

Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. The recent introduction of benchmarks raises the question of how best to train and evaluate models for this multi-turn, multi-agent, long-horizon task. This paper contributes to that conversation, by arguing that imitation learning (IL) and related low-level metrics are actually misleading and do not align with the goals of embodied dialogue research and may hinder progress.We provide empirical comparisons of metrics, analysis of three models, and make suggestions for how the field might best progress. First, we observe that models trained with IL take spurious actions during evaluation. Second, we find that existing models fail to ground query utterances, which are essential for task completion. Third, we argue evaluation should focus on higher-level semantic goals. We will release code to additionally filter the data and benchmark models for improved evaluation.

null

10.18653/v1/2022.emnlp-main.635

null

27,765

inproceedings

akula-etal-2022-alfred

{ALFRED}-{L}: Investigating the Role of Language for Action Learning in Interactive Visual Environments

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.636/

Akula, Arjun and Gella, Spandana and Padmakumar, Aishwarya and Namazifar, Mahdi and Bansal, Mohit and Thomason, Jesse and Hakkani-Tur, Dilek

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9369--9378

Embodied Vision and Language Task Completion requires an embodied agent to interpret natural language instructions and egocentric visual observations to navigate through and interact with environments. In this work, we examine ALFRED, a challenging benchmark for embodied task completion, with the goal of gaining insight into how effectively models utilize language. We find evidence that sequence-to-sequence and transformer-based models trained on this benchmark are not sufficiently sensitive to changes in input language instructions. Next, we construct a new test split {--} ALFRED-L to test whether ALFRED models can generalize to task structures not seen during training that intuitively require the same types of language understanding required in ALFRED. Evaluation of existing models on ALFRED-L suggests that (a) models are overly reliant on the sequence in which objects are visited in typical ALFRED trajectories and fail to adapt to modifications of this sequence and (b) models trained with additional augmented trajectories are able to adapt relatively better to such changes in input language instructions.

null

10.18653/v1/2022.emnlp-main.636

null

27,766

inproceedings

callison-burch-etal-2022-dungeons

Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.637/

Callison-Burch, Chris and Tomar, Gaurav Singh and Martin, Lara J. and Ippolito, Daphne and Bailis, Suma and Reitter, David

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9379--9393

AI researchers have posited Dungeons and Dragons (D{\&}D) as a challenge problem to test systems on various language-related capabilities. In this paper, we frame D{\&}D specifically as a dialogue system challenge, where the tasks are to both generate the next conversational turn in the game and predict the state of the game given the dialogue history. We create a gameplay dataset consisting of nearly 900 games, with a total of 7,000 players, 800,000 dialogue turns, 500,000 dice rolls, and 58 million words. We automatically annotate the data with partial state information about the game play. We train a large language model (LM) to generate the next game turn, conditioning it on different information. The LM can respond as a particular character or as the player who runs the game{---}i.e., the Dungeon Master (DM). It is trained to produce dialogue that is either in-character (roleplaying in the fictional world) or out-of-character (discussing rules or strategy). We perform a human evaluation to determine what factors make the generated output plausible and interesting. We further perform an automatic evaluation to determine how well the model can predict the game state given the history and examine how well tracking the game state improves its ability to produce plausible conversational output.

null

10.18653/v1/2022.emnlp-main.637

null

27,767

inproceedings

cho-etal-2022-unsupervised

Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.638/

Cho, Young Min and Zhang, Li and Callison-Burch, Chris

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9394--9401

Entity linking, the task of linking potentially ambiguous mentions in texts to corresponding knowledge-base entities, is an important component for language understanding. We address two challenge in entity linking: how to leverage wider contexts surrounding a mention, and how to deal with limited training data. We propose a fully unsupervised model called SumMC that first generates a guided summary of the contexts conditioning on the mention, and then casts the task to a multiple-choice problem where the model chooses an entity from a list of candidates. In addition to evaluating our model on existing datasets that focus on named entities, we create a new dataset that links noun phrases from WikiHow to Wikidata. We show that our SumMC model achieves state-of-the-art unsupervised performance on our new dataset and on exiting datasets.

null

10.18653/v1/2022.emnlp-main.638

null

27,768

inproceedings

chen-etal-2022-weakly

Weakly-Supervised Temporal Article Grounding

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.639/

Chen, Long and Niu, Yulei and Chen, Brian and Lin, Xudong and Han, Guangxing and Thomas, Christopher and Ayyubi, Hammad and Ji, Heng and Chang, Shih-Fu

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9402--9413

Given a long untrimmed video and natural language queries, video grounding (VG) aims to temporally localize the semantically-aligned video segments. Almost all existing VG work holds two simple but unrealistic assumptions: 1) All query sentences can be grounded in the corresponding video. 2) All query sentences for the same video are always at the same semantic scale. Unfortunately, both assumptions make today`s VG models fail to work in practice. For example, in real-world multimodal assets (eg, news articles), most of the sentences in the article can not be grounded in their affiliated videos, and they typically have rich hierarchical relations (ie, at different semantic scales). To this end, we propose a new challenging grounding task: Weakly-Supervised temporal Article Grounding (WSAG). Specifically, given an article and a relevant video, WSAG aims to localize all {\textquotedblleft}groundable{\textquotedblright} sentences to the video, and these sentences are possibly at different semantic scales. Accordingly, we collect the first WSAG dataset to facilitate this task: YouwikiHow, which borrows the inherent multi-scale descriptions in wikiHow articles and plentiful YouTube videos. In addition, we propose a simple but effective method DualMIL for WSAG, which consists of a two-level MIL loss and a single-/cross- sentence constraint loss. These training objectives are carefully designed for these relaxed assumptions. Extensive ablations have verified the effectiveness of DualMIL.

null

10.18653/v1/2022.emnlp-main.639

null

27,769

inproceedings

dong-etal-2022-exploring

Exploring Dual Encoder Architectures for Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.640/

Dong, Zhe and Ni, Jianmo and Bikel, Dan and Alfonseca, Enrique and Wang, Yuan and Qu, Chen and Zitouni, Imed

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9414--9419

Dual encoders have been used for question-answering (QA) and information retrieval (IR) tasks with good results. There are two major types of dual encoders, Siamese Dual Encoders (SDE), with parameters shared across two encoders, and Asymmetric Dual Encoder (ADE), with two distinctly parameterized encoders. In this work, we explore the dual encoder architectures for QA retrieval tasks. By evaluating on MS MARCO, open domain NQ, and the MultiReQA benchmarks, we show that SDE performs significantly better than ADE. We further propose three different improved versions of ADEs. Based on the evaluation of QA retrieval tasks and direct analysis of the embeddings, we demonstrate that sharing parameters in projection layers would enable ADEs to perform competitively with SDEs.

null

10.18653/v1/2022.emnlp-main.640

null

27,770

inproceedings

jiang-etal-2022-arxivedits

ar{X}iv{E}dits: Understanding the Human Revision Process in Scientific Writing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.641/

Jiang, Chao and Xu, Wei and Stevens, Samuel

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9420--9435

Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance. However, prior work studying the human editing process in this domain mainly focused on the abstract or introduction sections, resulting in an incomplete picture. In this work, we provide a complete computational framework for studying text revision in scientific writing. We first introduce arXivEdits, a new annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1,000 sentence pairs. It supports our data-driven analysis to unveil the common strategies practiced by researchers for revising their papers. To scale up the analysis, we also develop automatic methods to extract revision at document-, sentence-, and word-levels. A neural CRF sentence alignment model trained on our corpus achieves 93.8 F1, enabling the reliable matching of sentences between different versions. We formulate the edit extraction task as a span alignment problem, and our proposed method extracts more fine-grained and explainable edits, compared to the commonly used diff algorithm. An intention classifier trained on our dataset achieves 78.9 F1 on the fine-grained intent classification task. Our data and system are released at tiny.one/arxivedits.

null

10.18653/v1/2022.emnlp-main.641

null

27,771

inproceedings

zhan-etal-2022-feel

Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.642/

Zhan, Hongli and Sosea, Tiberiu and Caragea, Cornelia and Li, Junyi Jessy

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9436--9453

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people`s emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of {\textasciitilde}1,900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.

null

10.18653/v1/2022.emnlp-main.642

null

27,772

inproceedings

liang-etal-2022-analogical

Analogical Math Word Problems Solving with Enhanced Problem-Solution Association

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.643/

Liang, Zhenwen and Zhang, Jipeng and Zhang, Xiangliang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9454--9464

Math word problem (MWP) solving is an important task in question answering which requires human-like reasoning ability. Analogical reasoning has long been used in mathematical education, as it enables students to apply common relational structures of mathematical situations to solve new problems. In this paper, we propose to build a novel MWP solver by leveraging analogical MWPs, which advance the solver`s generalization ability across different kinds of MWPs. The key idea, named analogy identification, is to associate the analogical MWP pairs in a latent space, i.e., encoding an MWP close to another analogical MWP, while leaving away from the non-analogical ones. Moreover, a solution discriminator is integrated into the MWP solver to enhance the association between an MWP and its true solution. The evaluation results verify that our proposed analogical learning strategy promotes the performance of MWP-BERT on Math23k over the state-of-the-art model Generate2Rank, with 5 times fewer parameters in the encoder. We also find that our model has a stronger generalization ability in solving difficult MWPs due to the analogical learning from easy MWPs.

null

10.18653/v1/2022.emnlp-main.643

null

27,773

inproceedings

dalvi-mishra-etal-2022-towards

Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.644/

Dalvi Mishra, Bhavana and Tafjord, Oyvind and Clark, Peter

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9465--9480

Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. Our approach is to augment a QA model with a dynamic memory of user feedback, containing user-supplied corrections toerroneous model beliefs that users identify during interaction. Retrievals from memory are used as additional context for QA, to help avoid previous mistakes in similar new situations - a novel application of memory-based continuous learning. With simulated feedback, we find that our system (called TeachMe) continually improves with time, and without model retraining, requiring feedback on only 25{\%} of training examples to reach within 1{\%} of the upper-bound (feedback on all examples). Similarly, in experiments with real users, we observe a similar trend, with performance improving by over 15{\%} on a hidden test set after teaching. This suggests new opportunities for using frozen language models in an interactive setting where users can inspect, debug, and correct the model`s beliefs, leading to improved system`s performance over time.

null

10.18653/v1/2022.emnlp-main.644

null

27,774

inproceedings

gabburo-etal-2022-knowledge

Knowledge Transfer from Answer Ranking to Answer Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.645/

Gabburo, Matteo and Koncel-Kedziorski, Rik and Garg, Siddhant and Soldaini, Luca and Moschitti, Alessandro

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9481--9495

Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This allows for synthesizing the information from multiple candidates into a concise, natural-sounding answer. However, creating large-scale supervised training data for GenQA models is very challenging. In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue. First, we use an AS2 model to produce a ranking over answer candidates for a set of questions. Then, we use the top ranked candidate as the generation target, and the next k top ranked candidates as context for training a GenQA model. We also propose to use the AS2 model prediction scores for loss weighting and score-conditioned input/output shaping, to aid the knowledge transfer. Our evaluation on three public and one large industrial datasets demonstrates the superiority of our approach over the AS2 baseline, and GenQA trained using supervised data.

null

10.18653/v1/2022.emnlp-main.645

null

27,775

inproceedings

qian-etal-2022-perturbation

Perturbation Augmentation for Fairer {NLP}

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.646/

Qian, Rebecca and Ross, Candace and Fernandes, Jude and Smith, Eric Michael and Kiela, Douwe and Williams, Adina

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9496--9521

Unwanted and often harmful social biases are becoming ever more salient in NLP research, affecting both models and datasets. In this work, we ask whether training on demographically perturbed data leads to fairer language models. We collect a large dataset of human annotated text perturbations and train a neural perturbation model, which we show outperforms heuristic alternatives. We find that (i) language models (LMs) pre-trained on demographically perturbed corpora are typically more fair, and (ii) LMs finetuned on perturbed GLUE datasets exhibit less demographic bias on downstream tasks, and (iii) fairness improvements do not come at the expense of performance on downstream tasks. Lastly, we discuss outstanding questions about how best to evaluate the (un)fairness of large language models. We hope that this exploration of neural demographic perturbation will help drive more improvement towards fairer NLP.

null

10.18653/v1/2022.emnlp-main.646

null

27,776

inproceedings

feng-etal-2022-automatic

Automatic Document Selection for Efficient Encoder Pretraining

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.647/

Feng, Yukun and Xia, Patrick and Van Durme, Benjamin and Sedoc, Jo{\~a}o

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9522--9530

Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining.

null

10.18653/v1/2022.emnlp-main.647

null

27,777

inproceedings

yaari-etal-2022-aligned

The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.648/

Yaari, Adam and DeWitt, Jan and Hu, Henry and Stankovits, Bennett and Felshin, Sue and Berzak, Yevgeni and Aparicio, Helena and Katz, Boris and Cases, Ignacio and Barbu, Andrei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9531--9539

Treebanks have traditionally included only text and were derived from written sources such as newspapers or the web. We introduce the Aligned Multimodal Movie Treebank (AMMT), an English language treebank derived from dialog in Hollywood movies which includes transcriptions of the audio-visual streams with word-level alignment, as well as part of speech tags and dependency parses in the Universal Dependencies formalism. AMMT consists of 31,264 sentences and 218,090 words, that will amount to the 3rd largest UD English treebank and the only multimodal treebank in UD. To help with the web-based annotation effort, we also introduce the Efficient Audio Alignment Annotator (EAAA), a companion tool that enables annotators to significantly speed-up their annotation processes.

null

10.18653/v1/2022.emnlp-main.648

null

27,778

inproceedings

karpinska-etal-2022-demetr

{DEMETR}: Diagnosing Evaluation Metrics for Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.649/

Karpinska, Marzena and Raj, Nishant and Thai, Katherine and Song, Yixiao and Gupta, Ankita and Iyyer, Mohit

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9540--9561

While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particular candidate translation can be traced back to the presence or absence of certain words. The operations of newer learned metrics (e.g., BLEURT, COMET), which leverage pretrained language models to achieve higher correlations with human quality judgments than BLEU, are opaque in comparison. In this paper, we shed light on the behavior of these learned metrics by creating DEMETR, a diagnostic dataset with 31K English examples (translated from 10 source languages) for evaluating the sensitivity of MT evaluation metrics to 35 different linguistic perturbations spanning semantic, syntactic, and morphological error categories. All perturbations were carefully designed to form minimal pairs with the actual translation (i.e., differ in only one aspect). We find that learned metrics perform substantially better than string-based metrics on DEMETR. Additionally, learned metrics differ in their sensitivity to various phenomena (e.g., BERTScore is sensitive to untranslated words but relatively insensitive to gender manipulation, while COMET is much more sensitive to word repetition than to aspectual changes). We publicly release DEMETR to spur more informed future development of machine translation evaluation metrics

null

10.18653/v1/2022.emnlp-main.649

null

27,779

inproceedings

hu-etal-2022-empowering

Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.650/

Hu, Ziniu and Xu, Yichong and Yu, Wenhao and Wang, Shuohang and Yang, Ziyi and Zhu, Chenguang and Chang, Kai-Wei and Sun, Yizhou

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9562--9581

Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external knowledge sources, such as knowledge graphs, are often used to augment LMs. In this work, we propose knOwledge REasOning empowered Language Model(OREO-LM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. In this way, LM guides KG to walk towards the desired answer, while the retrieved knowledge improves LM.By adopting OREO-LM to RoBERTa and T5, we show significant performance gain, achieving state-of-art results in the Closed-Book setting. The performance enhancement is mainly from the KG reasoning`s capacity to infer missing relational facts. In addition, OREO-LM provides reasoning paths as rationales to interpret the model`s decision.

null

10.18653/v1/2022.emnlp-main.650

null

27,780

inproceedings

gaci-etal-2022-debiasing

Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.651/

Gaci, Yacine and Benatallah, Boualem and Casati, Fabio and Benabdeslem, Khalid

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9582--9602

Natural Language Processing (NLP) models are found to exhibit discriminatory stereotypes across many social constructs, e.g. gender and race. In comparison to the progress made in reducing bias from static word embeddings, fairness in sentence-level text encoders received little consideration despite their wider applicability in contemporary NLP tasks. In this paper, we propose a debiasing method for pre-trained text encoders that both reduces social stereotypes, and inflicts next to no semantic damage. Unlike previous studies that directly manipulate the embeddings, we suggest to dive deeper into the operation of these encoders, and pay more attention to the way they pay attention to different social groups. We find that stereotypes are also encoded in the attention layer. Then, we work on model debiasing by redistributing the attention scores of a text encoder such that it forgets any preference to historically advantaged groups, and attends to all social classes with the same intensity. Our experiments confirm that reducing bias from attention effectively mitigates it from the model`s text representations.

null

10.18653/v1/2022.emnlp-main.651

null

27,781

inproceedings

pouran-ben-veyseh-etal-2022-mee

{MEE}: A Novel Multilingual Event Extraction Dataset

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.652/

Pouran Ben Veyseh, Amir and Ebrahimi, Javid and Dernoncourt, Franck and Nguyen, Thien

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9603--9613

Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE. To foster future research in this direction, our dataset will be publicly available.

null

10.18653/v1/2022.emnlp-main.652

null

27,782

inproceedings

sanyal-etal-2022-robustlr

{R}obust{LR}: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.653/

Sanyal, Soumya and Liao, Zeyi and Ren, Xiang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9614--9631

Transformers have been shown to be able to perform deductive reasoning on inputs containing rules and statements written in the English natural language. However, it is unclear if these models indeed follow rigorous logical reasoning to arrive at the prediction or rely on spurious correlation patterns in making decisions. A strong deductive reasoning model should consistently understand the semantics of different logical operators. To this end, we present RobustLR, a diagnostic benchmark that evaluates the robustness of language models to minimal logical edits in the inputs and different logical equivalence conditions. In our experiments with RoBERTa, T5, and GPT3 we show that the models trained on deductive reasoning datasets do not perform consistently on the RobustLR test set, thus showing that the models are not robust to our proposed logical perturbations. Further, we observe that the models find it especially hard to learn logical negation operators. Our results demonstrate the shortcomings of current language models in logical reasoning and call for the development of better inductive biases to teach the logical semantics to language models. All the datasets and code base have been made publicly available.

null

10.18653/v1/2022.emnlp-main.653

null

27,783

inproceedings

wan-bansal-2022-evaluating

Evaluating and Improving Factuality in Multimodal Abstractive Summarization

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.654/

Wan, David and Bansal, Mohit

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9632--9648

Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modality and thus are not adequate for vision-and-language summarization. We propose CLIPBERTSCORE, a simple weighted combination of CLIPScore and BERTScore to leverage the robustness and strong factuality detection performance between image-summary and document-summary, respectively. Next, due to the lack of meta-evaluation benchmarks to evaluate the quality of multimodal factuality metrics, we collect human judgments of factuality with respect to documents and images. We show that this simple combination of two metrics in the zero-shot setting achieves higher correlations than existing factuality metrics for document summarization, outperforms an existing multimodal summarization metric, and performs competitively with strong multimodal factuality metrics specifically fine-tuned for the task. Our thorough analysis demonstrates the robustness and high correlation of CLIPBERTSCORE and its components on four factuality metric-evaluation benchmarks. Finally, we demonstrate two practical downstream applications of our CLIPBERTSCORE metric: for selecting important images to focus on during training, and as a reward for reinforcement learning to improve factuality of multimodal summary generation w.r.t automatic and human evaluation.

null

10.18653/v1/2022.emnlp-main.654

null

27,784

inproceedings

sclar-etal-2022-referee

Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.655/

Sclar, Melanie and West, Peter and Kumar, Sachin and Tsvetkov, Yulia and Choi, Yejin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9649--9668

We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.

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10.18653/v1/2022.emnlp-main.655

null

27,785

inproceedings

butoi-etal-2022-algorithms

Algorithms for Weighted Pushdown Automata

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.656/

Butoi, Alexandra and DuSell, Brian and Vieira, Tim and Cotterell, Ryan and Chiang, David

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

9669--9680

Weighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing. As most existing dynamic programming algorithms are designed for context-free grammars (CFGs), algorithms for PDAs often resort to a PDA-to-CFG conversion. In this paper, we develop novel algorithms that operate directly on WPDAs. Our algorithms are inspired by Lang`s algorithm, but use a more general definition of pushdown automaton and either reduce the space requirements by a factor of |Gamma| (the size of the stack alphabet) or reduce the runtime by a factor of more than |Q| (the number of states). When run on the same class of PDAs as Lang`s algorithm, our algorithm is both more space-efficient by a factor of |Gamma| and more time-efficient by a factor of |Q| x |Gamma|.

null

10.18653/v1/2022.emnlp-main.656

null

27,786