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

inproceedings

andrews-etal-2022-stopes

stopes - Modular Machine Translation Pipelines

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.26/

Andrews, Pierre and Wenzek, Guillaume and Heffernan, Kevin and {\c{C}}elebi, Onur and Sun, Anna and Kamran, Ammar and Guo, Yingzhe and Mourachko, Alexandre and Schwenk, Holger and Fan, Angela

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

258--265

Neural machine translation, as other natural language deep learning applications, is hungry for data. As research evolves, the data pipelines supporting that research evolve too, oftentimes re-implementing the same core components. Despite the potential of modular codebases, researchers have but little time to put code structure and reusability first. Unfortunately, this makes it very hard to publish clean, reproducible code to benefit a wider audience. In this paper, we motivate and describe stopes , a framework that addresses these issues while empowering scalability and versatility for research use cases. This library was a key enabler of the No Language Left Behind project, establishing new state of the art performance for a multilingual machine translation model covering 200 languages. stopes and the pipelines described are released under the MIT license at \url{https://github.com/facebookresearch/stopes}.

null

10.18653/v1/2022.emnlp-demos.26

null

27,992

inproceedings

gehrmann-etal-2022-gemv2

{GEM}v2: Multilingual {NLG} Benchmarking in a Single Line of Code

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.27/

Gehrmann, Sebastian and Bhattacharjee, Abhik and Mahendiran, Abinaya and Wang, Alex and Papangelis, Alexandros and Madaan, Aman and Mcmillan-major, Angelina and Shvets, Anna and Upadhyay, Ashish and Bohnet, Bernd and Yao, Bingsheng and Wilie, Bryan and Bhagavatula, Chandra and You, Chaobin and Thomson, Craig and Garbacea, Cristina and Wang, Dakuo and Deutsch, Daniel and Xiong, Deyi and Jin, Di and Gkatzia, Dimitra and Radev, Dragomir and Clark, Elizabeth and Durmus, Esin and Ladhak, Faisal and Ginter, Filip and Winata, Genta Indra and Strobelt, Hendrik and Hayashi, Hiroaki and Novikova, Jekaterina and Kanerva, Jenna and Chim, Jenny and Zhou, Jiawei and Clive, Jordan and Maynez, Joshua and Sedoc, Jo{\~a}o and Juraska, Juraj and Dhole, Kaustubh and Chandu, Khyathi Raghavi and Beltrachini, Laura Perez and Ribeiro, Leonardo F . R. and Tunstall, Lewis and Zhang, Li and Pushkarna, Mahim and Creutz, Mathias and White, Michael and Kale, Mihir Sanjay and Eddine, Moussa Kamal and Daheim, Nico and Subramani, Nishant and Dusek, Ondrej and Liang, Paul Pu and Ammanamanchi, Pawan Sasanka and Zhu, Qi and Puduppully, Ratish and Kriz, Reno and Shahriyar, Rifat and Cardenas, Ronald and Mahamood, Saad and Osei, Salomey and Cahyawijaya, Samuel and {\v{S}}tajner, Sanja and Montella, Sebastien and Jolly, Shailza and Mille, Simon and Hasan, Tahmid and Shen, Tianhao and Adewumi, Tosin and Raunak, Vikas and Raheja, Vipul and Nikolaev, Vitaly and Tsai, Vivian and Jernite, Yacine and Xu, Ying and Sang, Yisi and Liu, Yixin and Hou, Yufang

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

266--281

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances. We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other`s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.

null

10.18653/v1/2022.emnlp-demos.27

null

27,993

inproceedings

chowdhury-etal-2022-kgi

{KGI}: An Integrated Framework for Knowledge Intensive Language Tasks

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.28/

Chowdhury, Md Faisal Mahbub and Glass, Michael and Rossiello, Gaetano and Gliozzo, Alfio and Mihindukulasooriya, Nandana

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

282--288

In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking. Moreover, given a user query, we show how the output from these different models can be combined to cross-examine the outputs of each other. Particularly, we show how accuracy in dialogue can be improved using the question answering model. We are also releasing all models used in the demo as a contribution of this paper. A short video demonstrating the system is available at \url{https://ibm.box.com/v/emnlp2022-demos}.

null

10.18653/v1/2022.emnlp-demos.28

null

27,994

inproceedings

bianchi-etal-2022-twitter

{T}witter-Demographer: A Flow-based Tool to Enrich {T}witter Data

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.29/

Bianchi, Federico and Cutrona, Vincenzo and Hovy, Dirk

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

289--297

Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual data alone are often not enough to conduct studies: especially, social scientists need more variables to perform their analysis and control for various factors. How we augment this information, such as users' location, age, or tweet sentiment, has ramifications for anonymity and reproducibility, and requires dedicated effort. This paper describes Twitter-Demographer, a simple, flow-based tool to enrich Twitter data with additional information about tweets and users. {\textbackslash}tool is aimed at NLP practitioners, psycho-linguists, and (computational) social scientists who want to enrich their datasets with aggregated information, facilitating reproducibility, and providing algorithmic privacy-by-design measures for pseudo-anonymity. We discuss our design choices, inspired by the flow-based programming paradigm, to use black-box components that can easily be chained together and extended. We also analyze the ethical issues related to the use of this tool, and the built-in measures to facilitate pseudo-anonymity.

null

10.18653/v1/2022.emnlp-demos.29

null

27,995

inproceedings

gauthier-melancon-etal-2022-azimuth

Azimuth: Systematic Error Analysis for Text Classification

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.30/

Gauthier-melancon, Gabrielle and Marquez Ayala, Orlando and Brin, Lindsay and Tyler, Chris and Branchaud-charron, Frederic and Marinier, Joseph and Grande, Karine and Le, Di

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

298--310

We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.

null

10.18653/v1/2022.emnlp-demos.30

null

27,996

inproceedings

bai-etal-2022-synkb

{S}yn{KB}: Semantic Search for Synthetic Procedures

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.31/

Bai, Fan and Ritter, Alan and Madrid, Peter and Freitag, Dayne and Niekrasz, John

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

311--318

In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.

null

10.18653/v1/2022.emnlp-demos.31

null

27,997

inproceedings

obeid-etal-2022-camelira

Camelira: An {A}rabic Multi-Dialect Morphological Disambiguator

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.32/

Obeid, Ossama and Inoue, Go and Habash, Nizar

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

319--326

We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine.Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. Our system also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of a dialect identification component. Camelira is publicly accessible at \url{http://camelira.camel-lab.com}.

null

10.18653/v1/2022.emnlp-demos.32

null

27,998

inproceedings

pei-etal-2022-potato

{POTATO}: The Portable Text Annotation Tool

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.33/

Pei, Jiaxin and Ananthasubramaniam, Aparna and Wang, Xingyao and Zhou, Naitian and Dedeloudis, Apostolos and Sargent, Jackson and Jurgens, David

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

327--337

We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at \url{https://github.com/davidjurgens/potato} and will continue to be updated.

null

10.18653/v1/2022.emnlp-demos.33

null

27,999

inproceedings

widjaja-etal-2022-kgxboard

{KG}x{B}oard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.34/

Widjaja, Haris and Gashteovski, Kiril and Ben Rim, Wiem and Liu, Pengfei and Malon, Christopher and Ruffinelli, Daniel and Lawrence, Carolin and Neubig, Graham

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

338--350

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p; ?) or (?; p; t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannotreveal what exactly a model has learned {---} or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.

null

10.18653/v1/2022.emnlp-demos.34

null

28,000

inproceedings

goyal-etal-2022-falte

{FALTE}: A Toolkit for Fine-grained Annotation for Long Text Evaluation

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.35/

Goyal, Tanya and Li, Junyi Jessy and Durrett, Greg

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

351--358

A growing swath of NLP research is tackling problems related to generating long text, including tasks such as open-ended story generation, summarization, dialogue, and more. However, we currently lack appropriate tools to evaluate these long outputs of generation models: classic automatic metrics such as ROUGE have been shown to perform poorly, and newer learned metrics do not necessarily work wellfor all tasks and domains of text. Human rating and error analysis remains a crucial component for any evaluation of long text generation. In this paper, we introduce FALTE, a web-based annotation toolkit designed to address this shortcoming. Our tool allows researchers to collect fine-grained judgments of text quality from crowdworkers using an error taxonomy specific to the downstream task. Using the taskinterface, annotators can select and assign error labels to text span selections in an incremental paragraph-level annotation workflow. The latter functionality is designed to simplify the document-level task into smaller units and reduce cognitive load on the annotators. Our tool has previously been used to run a large-scale annotation study that evaluates the coherence of long generated summaries, demonstrating its utility.

null

10.18653/v1/2022.emnlp-demos.35

null

28,001

inproceedings

rajani-etal-2022-seal

{SEAL}: Interactive Tool for Systematic Error Analysis and Labeling

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.36/

Rajani, Nazneen and Liang, Weixin and Chen, Lingjiao and Mitchell, Margaret and Zou, James

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

359--370

With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance. However, many times these models systematically fail on tail data or rare groups not obvious in aggregate evaluation. Identifying such problematic data groups is even more challenging when there are no explicit labels (e.g., ethnicity, gender, etc.) and further compounded for NLP datasets due to the lack of visual features to characterize failure modes (e.g., Asian males, animals indoors, waterbirds on land etc.). This paper introduces an interactive Systematic Error Analysis and Labeling (SEAL) tool that uses a two-step approach to first identify high-error slices of data and then, in the second step, introduce methods to give human-understandable semantics to those underperforming slices. We explore a variety of methods for coming up with coherent semantics for the error groups using language models for semantic labeling and a text-to-image model for generating visual features.SEAL is available at \url{https://huggingface.co/spaces/nazneen/seal}.

null

10.18653/v1/2022.emnlp-demos.36

null

28,002

inproceedings

pacheco-etal-2022-hands

Hands-On Interactive Neuro-Symbolic {NLP} with {DR}ai{L}

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.37/

Pacheco, Maria Leonor and Roy, Shamik and Goldwasser, Dan

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

371--378

We recently introduced DRaiL, a declarative neural-symbolic modeling framework designed to support a wide variety of NLP scenarios. In this paper, we enhance DRaiL with an easy to use Python interface, equipped with methods to define, modify and augment DRaiL models interactively, as well as with methods to debug and visualize the predictions made. We demonstrate this interface with a challenging NLP task: predicting sentence and entity level moral sentiment in political tweets.

null

10.18653/v1/2022.emnlp-demos.37

null

28,003

inproceedings

wieting-etal-2022-paraphrastic

Paraphrastic Representations at Scale

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.38/

Wieting, John and Gimpel, Kevin and Neubig, Graham and Berg-kirkpatrick, Taylor

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

379--388

We present a system that allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. We release trained models for English, Arabic, German, Spanish, French, Russian, Turkish, and Chinese. We train these models on large amounts of data, achieving significantly improved performance from our original papers on a suite of monolingual semantic similarity, cross-lingual semantic similarity, and bitext mining tasks. Moreover, the resulting models surpass all prior work on efficient unsupervised semantic textual similarity, even significantly outperforming supervised BERT-based models like Sentence-BERT (Reimers and Gurevych, 2019). Most importantly, our models are orders of magnitude faster than other strong similarity models and can be used on CPU with little difference in inference speed (even improved speed over GPU when using more CPU cores), making these models an attractive choice for users without access to GPUs or for use on embedded devices. Finally, we add significantly increased functionality to the code bases for training paraphrastic sentence models, easing their use for both inference and for training them for any desired language with parallel data. We also include code to automatically download and preprocess training data.

null

10.18653/v1/2022.emnlp-demos.38

null

28,004

inproceedings

razeghi-etal-2022-snoopy

Snoopy: An Online Interface for Exploring the Effect of Pretraining Term Frequencies on Few-Shot {LM} Performance

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.39/

Razeghi, Yasaman and Mekala, Raja Sekhar Reddy and Logan Iv, Robert L and Gardner, Matt and Singh, Sameer

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

389--395

Current evaluation schemes for large language models often fail to consider the impact of the overlap between pretraining corpus and test data on model performance statistics. Snoopy is an online interface that allows researchers to study this impact in few-shot learning settings. Our demo provides term frequency statistics for the Pile, which is an 800 GB corpus, accompanied by the precomputed performance of EleutherAI/GPT models on more than 20 NLP benchmarks, including numerical, commonsense reasoning, natural language understanding, and question-answering tasks. Snoopy allows a user to interactively align specific terms in test instances with their frequency in the Pile, enabling exploratory analysis of how term frequency is related to the accuracy of the models, which are hard to discover through automated means. A user can look at correlations over various model sizes and numbers of in-context examples and visualize the result across multiple (potentially aggregated) datasets. Using Snoopy, we show that a researcher can quickly replicate prior analyses for numerical tasks, while simultaneously allowing for much more expansive exploration that was previously challenging. Snoopy is available at \url{https://nlp.ics.uci.edu/snoopy}.

null

10.18653/v1/2022.emnlp-demos.39

null

28,005

inproceedings

zhang-etal-2022-bmcook

{BMC}ook: A Task-agnostic Compression Toolkit for Big Models

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.40/

Zhang, Zhengyan and Gong, Baitao and Chen, Yingfa and Han, Xu and Zeng, Guoyang and Zhao, Weilin and Chen, Yanxu and Liu, Zhiyuan and Sun, Maosong

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

396--405

Recently, pre-trained language models (PLMs) have achieved great success on various NLP tasks and have shown a trend of exponential growth in model size. To alleviate the unaffordable computational costs brought by the size growth, model compression has been widely explored. Existing efforts have achieved promising results in compressing medium-sized models for specific tasks, while task-agnostic compression for big models with over billions of parameters is rarely studied. Task-agnostic compression can provide an efficient and versatile big model for both prompting and delta tuning, leading to a more general impact than task-specific compression. Hence, we introduce a task-agnostic compression toolkit BMCook for big models. In BMCook, we implement four representative compression methods, including quantization, pruning, distillation, and MoEfication. Developers can easily combine these methods towards better efficiency. To evaluate BMCook, we apply it to compress T5-3B (a PLM with 3 billion parameters). We achieve nearly 12x efficiency improvement while maintaining over 97{\%} of the original T5-3B performance on three typical NLP benchmarks. Moreover, the final compressed model also significantly outperforms T5-base (a PLM with 220 million parameters), which has a similar computational cost. BMCook is publicly available at \url{https://github.com/OpenBMB/BMCook}.

null

10.18653/v1/2022.emnlp-demos.40

null

28,006

inproceedings

tsvigun-etal-2022-altoolbox

{ALT}oolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.41/

Tsvigun, Akim and Sanochkin, Leonid and Larionov, Daniil and Kuzmin, Gleb and Vazhentsev, Artem and Lazichny, Ivan and Khromov, Nikita and Kireev, Danil and Rubashevskii, Aleksandr and Shahmatova, Olga and Dylov, Dmitry V. and Galitskiy, Igor and Shelmanov, Artem

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

406--434

We present ALToolbox {--} an open-source framework for active learning (AL) annotation in natural language processing. Currently, the framework supports text classification, sequence tagging, and seq2seq tasks. Besides state-of-the-art query strategies, ALToolbox provides a set of tools that help to reduce computational overhead and duration of AL iterations and increase annotated data reusability. The framework aims to support data scientists and researchers by providing an easy-to-deploy GUI annotation tool directly in the Jupyter IDE and an extensible benchmark for novel AL methods. We prepare a small demonstration of ALToolbox capabilities available online. The code of the framework is published under the MIT license.

null

10.18653/v1/2022.emnlp-demos.41

null

28,007

inproceedings

tang-etal-2022-textbox

{T}ext{B}ox 2.0: A Text Generation Library with Pre-trained Language Models

Che, Wanxiang and Shutova, Ekaterina

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-demos.42/

Tang, Tianyi and Li, Junyi and Chen, Zhipeng and Hu, Yiwen and Yu, Zhuohao and Dai, Wenxun and Zhao, Wayne Xin and Nie, Jian-yun and Wen, Ji-rong

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

435--444

To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers 13 common text generation tasks and their corresponding 83 datasets and further incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. We also implement 4 efficient training strategies and provide 4 generation objectives for pre-training new PLMs from scratch. To be unified, we design the interfaces to support the entire research pipeline (from data loading to training and evaluation), ensuring that each step can be fulfilled in a unified way. Despite the rich functionality, it is easy to use our library, either through the friendly Python API or command line. To validate the effectiveness of our library, we conduct extensive experiments and exemplify four types of research scenarios. The project is released at the link: \url{https://github.com/RUCAIBox/TextBox#2.0}.

null

10.18653/v1/2022.emnlp-demos.42

null

28,008

inproceedings

fusco-etal-2022-unsupervised

Unsupervised Term Extraction for Highly Technical Domains

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.1/

Fusco, Francesco and Staar, Peter and Antognini, Diego

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

1--8

Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains requiring in-depth expertise are scarce and expensive to obtain. In this paper, we describe the term extraction subsystem of a commercial knowledge discovery platform that targets highly technical fields such as pharma, medical, and material science. To be able to generalize across domains, we introduce a fully unsupervised annotator (UA). It extracts terms by combining novel morphological signals from sub-word tokenization with term-to-topic and intra-term similarity metrics, computed using general-domain pre-trained sentence-encoders. The annotator is used to implement a weakly-supervised setup, where transformer-models are fine-tuned (or pre-trained) over the training data generated by running the UA over large unlabeled corpora. Our experiments demonstrate that our setup can improve the predictive performance while decreasing the inference latency on both CPUs and GPUs. Our annotators provide a very competitive baseline for all the cases where annotations are not available.

null

10.18653/v1/2022.emnlp-industry.1

null

28,010

inproceedings

sun-etal-2022-dynamar

{D}yna{M}a{R}: Dynamic Prompt with Mask Token Representation

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.2/

Sun, Xiaodi and Rajagopalan, Sunny and Nigam, Priyanka and Lu, Weiyi and Xu, Yi and Keivanloo, Iman and Zeng, Belinda and Chilimbi, Trishul

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

9--17

Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically when adapting these language models to downstream tasks, like a classification or regression task, we employ a fine-tuning paradigm in which the sentence representation from the language model is input to a task-specific head; the model is then fine-tuned end-to-end. However, with the emergence of models like GPT-3, prompt-based fine-tuning has been proven to be a successful approach for few-shot tasks. Inspired by this work, we study discrete prompt technologies in practice. There are two issues that arise with the standard prompt approach. First, it can overfit on the prompt template. Second, it requires manual effort to formulate the downstream task as a language model problem. In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues. We refer to our approach as DynaMaR {--} Dynamic Prompt with Mask Token Representation. Results show that DynaMaR can achieve an average improvement of 10{\%} in few-shot settings and improvement of 3.7{\%} in data-rich settings over the standard fine-tuning approach on four e-commerce applications.

null

10.18653/v1/2022.emnlp-industry.2

null

28,011

inproceedings

soltan-etal-2022-hybrid

A Hybrid Approach to Cross-lingual Product Review Summarization

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.3/

Soltan, Saleh and Soto, Victor and Tran, Ke and Hamza, Wael

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

18--28

We present a hybrid approach for product review summarization which consists of: (i) an unsupervised extractive step to extract the most important sentences out of all the reviews, and (ii) a supervised abstractive step to summarize the extracted sentences into a coherent short summary. This approach allows us to develop an efficient cross-lingual abstractive summarizer that can generate summaries in any language, given the extracted sentences out of thousands of reviews in a source language. In order to train and test the abstractive model, we create the Cross-lingual Amazon Reviews Summarization (CARS) dataset which provides English summaries for training, and English, French, Italian, Arabic, and Hindi summaries for testing based on selected English reviews. We show that the summaries generated by our model are as good as human written summaries in coherence, informativeness, non-redundancy, and fluency.

null

10.18653/v1/2022.emnlp-industry.3

null

28,012

inproceedings

ramamonjison-etal-2022-augmenting

Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.4/

Ramamonjison, Rindra and Li, Haley and Yu, Timothy and He, Shiqi and Rengan, Vishnu and Banitalebi-dehkordi, Amin and Zhou, Zirui and Zhang, Yong

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

29--62

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains.

null

10.18653/v1/2022.emnlp-industry.4

null

28,013

inproceedings

liu-etal-2022-knowledge

Knowledge Distillation based Contextual Relevance Matching for {E}-commerce Product Search

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.5/

Liu, Ziyang and Wang, Chaokun and Feng, Hao and Wu, Lingfei and Yang, Liqun

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

63--76

Online relevance matching is an essential task of e-commerce product search to boost the utility of search engines and ensure a smooth user experience. Previous work adopts either classical relevance matching models or Transformer-style models to address it. However, they ignore the inherent bipartite graph structures that are ubiquitous in e-commerce product search logs and are too inefficient to deploy online. In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models. Especially for the core student model of the framework, we propose a novel method using k-order relevance modeling. The experimental results on large-scale real-world data (the size is 6 174 million) show that the proposed method significantly improves the prediction accuracy in terms of human relevance judgment. We deploy our method to JD.com online search platform. The A/B testing results show that our method significantly improves most business metrics under price sort mode and default sort mode.

null

10.18653/v1/2022.emnlp-industry.5

null

28,014

inproceedings

purpura-etal-2022-accelerating

Accelerating the Discovery of Semantic Associations from Medical Literature: Mining Relations Between Diseases and Symptoms

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.6/

Purpura, Alberto and Bonin, Francesca and Bettencourt-silva, Joao

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

77--89

Medical literature is a vast and constantly expanding source of information about diseases, their diagnoses and treatments. One of the ways to extract insights from this type of data is through mining association rules between such entities. However, existing solutions do not take into account the semantics of sentences from which entity co-occurrences are extracted. We propose a scalable solution for the automated discovery of semantic associations between different entities such as diseases and their symptoms. Our approach employs the UMLS semantic network and a binary relation classification model trained with distant supervision to validate and help ranking the most likely entity associations pairs extracted with frequency-based association rule mining algorithms. We evaluate the proposed system on the task of extracting disease-symptom associations from a collection of over 14M PubMed abstracts and validate our results against a publicly available known list of disease-symptom pairs.

null

10.18653/v1/2022.emnlp-industry.6

null

28,015

inproceedings

uma-naresh-etal-2022-pentatron

{PENTATRON}: {PE}rsonalized co{NT}ext-Aware Transformer for Retrieval-based c{O}nversational u{N}derstanding

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.7/

Uma Naresh, Niranjan and Jiang, Ziyan and Ankit, Ankit and Lee, Sungjin and Hao, Jie and Fan, Xing and Guo, Chenlei

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

90--98

Conversational understanding is an integral part of modern intelligent devices. In a large fraction of the global traffic from customers using smart digital assistants, frictions in dialogues may be attributed to incorrect understanding of the entities in a customer`s query due to factors including ambiguous mentions, mispronunciation, background noise and faulty on-device signal processing. Such errors are compounded by two common deficiencies from intelligent devices namely, (1) the device not being tailored to individual customers, and (2) the device responses being unaware of the context in the conversation session. Viewing this problem via the lens of retrieval-based search engines, we build and evaluate a scalable entity correction system, PENTATRON. The system leverages a parametric transformer-based language model to learn patterns from in-session customer-device interactions coupled with a non-parametric personalized entity index to compute the correct query, which aids downstream components in reasoning about the best response. In addition to establishing baselines and demonstrating the value of personalized and context-aware systems, we use multitasking to learn the domain of the correct entity. We also investigate the utility of language model prompts. Through extensive experiments, we show a significant upward movement of the key metric (Exact Match) by up to 500.97{\%} (relative to the baseline).

null

10.18653/v1/2022.emnlp-industry.7

null

28,016

inproceedings

zhang-misra-2022-machine

Machine translation impact in {E}-commerce multilingual search

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.8/

Zhang, Bryan and Misra, Amita

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

99--109

Previous work suggests that performance of cross-lingual information retrieval correlates highly with the quality of Machine Translation. However, there may be a threshold beyond which improving query translation quality yields little or no benefit to further improve the retrieval performance. This threshold may depend upon multiple factors including the source and target languages, the existing MT system quality and the search pipeline. In order to identify the benefit of improving an MT system for a given search pipeline, we investigate the sensitivity of retrieval quality to the presence of different levels of MT quality using experimental datasets collected from actual traffic. We systematically improve the performance of our MT systems quality on language pairs as measured by MT evaluation metrics including Bleu and Chrf to determine their impact on search precision metrics and extract signals that help to guide the improvement strategies. Using this information we develop techniques to compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve.

null

10.18653/v1/2022.emnlp-industry.8

null

28,017

inproceedings

ding-etal-2022-ask

Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.9/

Ding, Yifan and Liang, Yan and Zalmout, Nasser and Li, Xian and Grant, Christan and Weninger, Tim

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

110--110

The product attribute value extraction (AVE) task aims to capture key factual information from product profiles, and is useful for several downstream applications in e-Commerce platforms. Previous contributions usually formulate this task using sequence labeling or reading comprehension architectures. However, sequence labeling models tend to be conservative in their predictions resulting in a high false negative rate. Existing reading comprehension formulations, on the other hand, can over-generate attribute values which hinders precision. In the present work we address these limitations with a new end-to-end pipeline framework called Ask-and-Verify. Given a product and an attribute query, the Ask step detects the top-K span candidates (i.e. possible attribute values) from the product profiles, then the Verify step filters out false positive candidates. We evaluate Ask-and-Verify model on Amazon`s product pages and AliExpress public dataset, and present a comparative analysis as well as a detailed ablation study. Despite its simplicity, we show that Ask-and-Verify outperforms recent state-of-the-art models by up to 3.1{\%} F1 absolute improvement points, while also scaling to thousands of attributes.

null

10.18653/v1/2022.emnlp-industry.9

null

28,018

inproceedings

savkov-etal-2022-consultation

Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.10/

Savkov, Aleksandar and Moramarco, Francesco and Papadopoulos Korfiatis, Alex and Perera, Mark and Belz, Anya and Reiter, Ehud

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

111--120

Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality. This difficulty is compounded in automatic consultation note generation by differing opinions between medical experts both about which patient statements should be included in generated notes and about their respective importance in arriving at a diagnosis. Previous real-world evaluations of note-generation systems saw substantial disagreement between expert evaluators. In this paper we propose a protocol that aims to increase objectivity by grounding evaluations in Consultation Checklists, which are created in a preliminary step and then used as a common point of reference during quality assessment. We observed good levels of inter-annotator agreement in a first evaluation study using the protocol; further, using Consultation Checklists produced in the study as reference for automatic metrics such as ROUGE or BERTScore improves their correlation with human judgements compared to using the original human note.

null

10.18653/v1/2022.emnlp-industry.10

null

28,019

inproceedings

do-etal-2022-towards

Towards Need-Based Spoken Language Understanding Model Updates: What Have We Learned?

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.11/

Do, Quynh and Gaspers, Judith and Sorokin, Daniil and Lehnen, Patrick

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

121--127

In productionized machine learning systems, online model performance is known to deteriorate over time when there is a distributional drift between offline training and online application data. As a remedy, models are typically retrained at fixed time intervals, implying high computational and manual costs. This work aims at decreasing such costs in productionized, large-scale Spoken Language Understanding systems. In particular, we develop a need-based re-training strategy guided by an efficient drift detector and discuss the arising challenges including system complexity, overlapping model releases, observation limitation and the absence of annotated resources at runtime. We present empirical results on historical data and confirm the utility of our design decisions via an online A/B experiment.

null

10.18653/v1/2022.emnlp-industry.11

null

28,020

inproceedings

peris-etal-2022-knowledge

Knowledge Distillation Transfer Sets and their Impact on Downstream {NLU} Tasks

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.12/

Peris, Charith and Tan, Lizhen and Gueudre, Thomas and Gojayev, Turan and Wei, Pan and Oz, Gokmen

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

128--137

Teacher-student knowledge distillation is a popular technique for compressing today`s prevailing large language models into manageable sizes that fit low-latency downstream applications. Both the teacher and the choice of transfer set used for distillation are crucial ingredients in creating a high quality student. Yet, the generic corpora used to pretrain the teacher and the corpora associated with the downstream target domain are often significantly different, which raises a natural question: should the student be distilled over the generic corpora, so as to learn from high-quality teacher predictions, or over the downstream task corpora to align with finetuning? Our study investigates this trade-off using Domain Classification (DC) and Intent Classification/Named Entity Recognition (ICNER) as downstream tasks. We distill several multilingual students from a larger multilingual LM with varying proportions of generic and task-specific datasets, and report their performance after finetuning on DC and ICNER. We observe significant improvements across tasks and test sets when only task-specific corpora is used. We also report on how the impact of adding task-specific data to the transfer set correlates with the similarity between generic and task-specific data. Our results clearly indicate that, while distillation from a generic LM benefits downstream tasks, students learn better using target domain data even if it comes at the price of noisier teacher predictions. In other words, target domain data still trumps teacher knowledge.

null

10.18653/v1/2022.emnlp-industry.12

null

28,021

inproceedings

aguirre-etal-2022-exploiting

Exploiting In-Domain Bilingual Corpora for Zero-Shot Transfer Learning in {NLU} of Intra-Sentential Code-Switching Chatbot Interactions

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.13/

Aguirre, Maia and Serras, Manex and Garc{\'i}a-sardi{\~n}a, Laura and L{\'o}pez-fern{\'a}ndez, Jacobo and M{\'e}ndez, Ariane and Del Pozo, Arantza

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

138--144

Code-switching (CS) is a very common phenomenon in regions with various co-existing languages. Since CS is such a frequent habit in informal communications, both spoken and written, it also arises naturally in Human-Machine Interactions. Therefore, in order for natural language understanding (NLU) not to be degraded, CS must be taken into account when developing chatbots. The co-existence of multiple languages in a single NLU model has become feasible with multilingual language representation models such as mBERT. In this paper, the efficacy of zero-shot cross-lingual transfer learning with mBERT for NLU is evaluated on a Basque-Spanish CS chatbot corpus, comparing the performance of NLU models trained using in-domain chatbot utterances in Basque and/or Spanish without CS. The results obtained indicate that training joint multi-intent classification and entity recognition models on both languages simultaneously achieves best performance, better capturing the CS patterns.

null

10.18653/v1/2022.emnlp-industry.13

null

28,022

inproceedings

wang-etal-2022-calibrating

Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.14/

Wang, Cheng and Balazs, Jorge and Szarvas, Gy{\"orgy and Ernst, Patrick and Poddar, Lahari and Danchenko, Pavel

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

145--153

Imbalanced data distribution is a practical and common challenge in building production-level machine learning (ML) models in industry, where data usually exhibits long-tail distributions. For instance, in virtual AI Assistants, such as Google Assistant, Amazon Alexa and Apple Siri, the {\textquotedblleft}play music{\textquotedblright} or {\textquotedblleft}set timer{\textquotedblright} utterance is exposed to an order of magnitude more traffic than other skills. This can easily cause trained models to overfit to the majority classes, categories or intents, lead to model miscalibration. The uncalibrated models output unreliable (mostly overconfident) predictions, which are at high risk of affecting downstream decision-making systems. In this work, we study the calibration of production models in the industry use-case of predicting product return reason codes in customer service conversations of an online retail store; The returns reasons also exhibit class imbalance. To alleviate the resulting miscalibration in the production ML model, we streamline the model development and deployment using focal loss (CITATION).We empirically show the effectiveness of model training with focal loss in learning better calibrated models, as compared to standard cross-entropy loss. Better calibration, in turn, enables better control of the precision-recall trade-off for the models deployed in production.

null

10.18653/v1/2022.emnlp-industry.14

null

28,023

inproceedings

garrido-ramas-etal-2022-unsupervised

Unsupervised training data re-weighting for natural language understanding with local distribution approximation

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.15/

Garrido Ramas, Jose and Le, Dieu-thu and Chen, Bei and Kumar, Manoj and Rottmann, Kay

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

154--160

One of the major challenges of training Natural Language Understanding (NLU) production models lies in the discrepancy between the distributions of the offline training data and of the online live data, due to, e.g., biased sampling scheme, cyclic seasonality shifts, annotated training data coming from a variety of different sources, and a changing pool of users. Consequently, the model trained by the offline data is biased. We often observe this problem especially in task-oriented conversational systems, where topics of interest and the characteristics of users using the system change over time. In this paper we propose an unsupervised approach to mitigate the offline training data sampling bias in multiple NLU tasks. We show that a local distribution approximation in the pre-trained embedding space enables the estimation of importance weights for training samples guiding re-sampling for an effective bias mitigation. We illustrate our novel approach using multiple NLU datasets and show improvements obtained without additional annotation, making this a general approach for mitigating effects of sampling bias.

null

10.18653/v1/2022.emnlp-industry.15

null

28,024

inproceedings

chiu-shinzato-2022-cross

Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.16/

Chiu, Justin and Shinzato, Keiji

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

161--168

Matching a seller listed item to an appropriate product is an important step for an e-commerce platform. With the recent advancement in deep learning, there are different encoder based approaches being proposed as solution. When textual data for two products are available, cross-encoder approaches encode them jointly while bi-encoder approaches encode them separately. Since cross-encoders are computationally heavy, approaches based on bi-encoders are a common practice for this challenge. In this paper, we propose cross-encoder data annotation; a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoder model. This technique enables us to build a robust model without annotation on newly collected training data or further improve model performance on annotated training data. We evaluate the cross-encoder data annotation on the product matching task using a real-world e-commerce dataset containing 104 million products. Experimental results show that the cross-encoder data annotation improves 4{\%} absolute accuracy when no annotation for training data is available, and 2{\%} absolute accuracy when annotation for training data is available.

null

10.18653/v1/2022.emnlp-industry.16

null

28,025

inproceedings

poddar-etal-2022-deploying

Deploying a Retrieval based Response Model for Task Oriented Dialogues

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.17/

Poddar, Lahari and Szarvas, Gy{\"orgy and Wang, Cheng and Balazs, Jorge and Danchenko, Pavel and Ernst, Patrick

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

169--178

Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a large set of response candidates. First, we provide a simple algorithm to semi-automatically create a high-coverage template set from historic conversations without any annotation. Second, we propose a neural architecture that encodes the dialogue context and applicable business constraints as profile features for ranking the next turn. Third, we describe a two-stage learning strategy with self-supervised training, followed by supervised fine-tuning on limited data collected through a human-in-the-loop platform. Finally, we describe offline experiments and present results of deploying our model with human-in-the-loop to converse with live customers online.

null

10.18653/v1/2022.emnlp-industry.17

null

28,026

inproceedings

vo-etal-2022-tackling

Tackling Temporal Questions in Natural Language Interface to Databases

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.18/

Vo, Ngoc Phuoc An and Popescu, Octavian and Manotas, Irene and Sheinin, Vadim

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

179--187

Temporal aspect is one of the most challenging areas in Natural Language Interface to Databases (NLIDB). This paper addresses and examines how temporal questions being studied and supported by the research community at both levels: popular annotated dataset (e.g. Spider) and recent advanced models. We present a new dataset with accompanied databases supporting temporal questions in NLIDB. We experiment with two SOTA models (Picard and ValueNet) to investigate how our new dataset helps these models learn and improve performance in temporal aspect.

null

10.18653/v1/2022.emnlp-industry.18

null

28,027

inproceedings

vishwanathan-etal-2022-multi

Multi-Tenant Optimization For Few-Shot Task-Oriented {FAQ} Retrieval

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.19/

Vishwanathan, Asha and Warrier, Rajeev and Vadakkekara Suresh, Gautham and Kandpal, Chandra Shekhar

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

188--197

Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis {\`a} vis community based FAQs. Each FAQ question represents an intent which is usually an umbrella term for many related user queries. We evaluate performance for such Business FAQs both with standard FAQ retrieval techniques using query-Question (q-Q) similarity and few-shot intent detection techniques. Implementing a real-world solution for FAQ retrieval in order to support multiple tenants (FAQ sets) entails optimizing speed, accuracy and cost. We propose a novel approach to scale multi-tenant FAQ applications in real-world context by contrastive fine-tuning of the last layer in sentence Bi-Encoders along with tenant-specific weight switching.

null

10.18653/v1/2022.emnlp-industry.19

null

28,028

inproceedings

dekeyser-etal-2022-iterative

Iterative Stratified Testing and Measurement for Automated Model Updates

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.20/

Dekeyser, Elizabeth and Comment, Nicholas and Pei, Shermin and Kumar, Rajat and Rai, Shruti and Wu, Fengtao and Haverty, Lisa and Shimizu, Kanna

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

198--205

Automating updates to machine learning systems is an important but understudied challenge in AutoML. The high model variance of many cutting-edge deep learning architectures means that retraining a model provides no guarantee of accurate inference on all sample types. To address this concern, we present Automated Data-Shape Stratified Model Updates (ADSMU), a novel framework that relies on iterative model building coupled with data-shape stratified model testing and improvement. Using ADSMU, we observed a 26{\%} (relative) improvement in accuracy for new model use cases on a large-scale NLU system, compared to a naive (manually) retrained baseline and current cutting-edge methods.

null

10.18653/v1/2022.emnlp-industry.20

null

28,029

inproceedings

gandhi-etal-2022-slate

{SLATE}: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.21/

Gandhi, Apurva and Serrao, Ryan and Fang, Biyi and Antonius, Gilbert and Hong, Jenna and Nguyen, Tra My and Yi, Sheng and Nosakhare, Ehi and Shaffer, Irene and Srinivasan, Soundararajan

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

206--217

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or {\textquotedblleft}inked{\textquotedblright}) notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4{\%}, a sentence segmentation (boundary similarity) score of 88.4{\%} and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.

null

10.18653/v1/2022.emnlp-industry.21

null

28,030

inproceedings

rabinovich-etal-2022-gaining

Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.22/

Rabinovich, Ella and Vetzler, Matan and Boaz, David and Kumar, Vineet and Pandey, Gaurav and Anaby Tavor, Ateret

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

218--225

The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems. The success of these systems is highly dependent on the accuracy of their intent identification {--} the process of deducing the goal or meaning of the user`s request and mapping it to one of the known intents for further processing. Gaining insights into unrecognized utterances {--} user requests the systems fails to attribute to a known intent {--} is therefore a key process in continuous improvement of goal-oriented dialog systems. We present an end-to-end pipeline for processing unrecognized user utterances, deployed in a real-world, commercial task-oriented dialog system, including a specifically-tailored clustering algorithm, a novel approach to cluster representative extraction, and cluster naming. We evaluated the proposed components, demonstrating their benefits in the analysis of unrecognized user requests.

null

10.18653/v1/2022.emnlp-industry.22

null

28,031

inproceedings

cao-etal-2022-cocoid

{C}o{C}o{ID}: Learning Contrastive Representations and Compact Clusters for Semi-Supervised Intent Discovery

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.23/

Cao, Qian and Xiong, Deyi and Wang, Qinlong and Peng, Xia

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

226--236

Intent discovery is to mine new intents from user utterances, which are not present in the set of manually predefined intents. Previous approaches to intent discovery usually automatically cluster novel intents with prior knowledge from intent-labeled data in a semi-supervised way. In this paper, we focus on the discriminative user utterance representation learning and the compactness of the learned intent clusters. We propose a novel semi-supervised intent discovery framework CoCoID with two essential components: contrastive user utterance representation learning and intra-cluster knowledge distillation. The former attempts to detect similar and dissimilar intents from a minibatch-wise perspective. The latter regularizes the predictive distribution of the model over samples in a cluster-wise way. We conduct experiments on both real-life challenging datasets (i.e., CLINC and BANKING) that are curated to emulate the true environment of commercial/production systems and traditional datasets (i.e., StackOverflow and DBPedia) to evaluate the proposed CoCoID. Experiment results demonstrate that our model substantially outperforms state-of-the-art intent discovery models (12 baselines) by over 1.4 ACC and ARI points and 1.1 NMI points across the four datasets. Further analyses suggest that CoCoID is able to learn contrastive representations and compact clusters for intent discovery.

null

10.18653/v1/2022.emnlp-industry.23

null

28,032

inproceedings

j-kurisinkel-chen-2022-tractable

Tractable {\&} Coherent Multi-Document Summarization: Discrete Optimization of Multiple Neural Modeling Streams via Integer Linear Programming

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.24/

J Kurisinkel, Litton and Chen, Nancy

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

237--243

One key challenge in multi-document summarization is the generated summary is often less coherent compared to single document summarization due to the larger heterogeneity of the input source content. In this work, we propose a generic framework to jointly consider coherence and informativeness in multi-document summarization and offers provisions to replace individual components based on the domain of source text. In particular, the framework characterizes coherence through verb transitions and entity mentions and takes advantage of syntactic parse trees and neural modeling for intra-sentential noise pruning. The framework cast the entire problem as an integer linear programming optimization problem with neural and non-neural models as linear components. We evaluate our method in the news and legal domains. The proposed approach consistently performs better than competitive baselines for both objective metrics and human evaluation.

null

10.18653/v1/2022.emnlp-industry.24

null

28,033

inproceedings

qiao-etal-2022-grafting

Grafting Pre-trained Models for Multimodal Headline Generation

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.25/

Qiao, Lingfeng and Wu, Chen and Liu, Ye and Peng, Haoyuan and Yin, Di and Ren, Bo

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

244--253

Multimodal headline utilizes both video frames and transcripts to generate the natural language title of the videos. Due to a lack of large-scale, manually annotated data, the task of annotating grounded headlines for video is labor intensive and impractical. Previous researches on pre-trained language models and video-language models have achieved significant progress in related downstream tasks. However, none of them can be directly applied to multimodal headline architecture where we need both multimodal encoder and sentence decoder. A major challenge in simply gluing language model and video-language model is the modality balance, which is aimed at combining visual-language complementary abilities. In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model. We also present a consensus fusion mechanism for the integration of different components, via inter/intra modality relation. Empirically, experiments show that the grafted model achieves strong results on a brand-new dataset collected from real-world applications.

null

10.18653/v1/2022.emnlp-industry.25

null

28,034

inproceedings

le-etal-2022-semi

Semi-supervised Adversarial Text Generation based on {S}eq2{S}eq models

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.26/

Le, Hieu and Le, Dieu-thu and Weber, Verena and Church, Chris and Rottmann, Kay and Bradford, Melanie and Chin, Peter

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

254--262

To improve deep learning models' robustness, adversarial training has been frequently used in computer vision with satisfying results. However, adversarial perturbation on text have turned out to be more challenging due to the discrete nature of text. The generated adversarial text might not sound natural or does not preserve semantics, which is the key for real world applications where text classification is based on semantic meaning. In this paper, we describe a new way for generating adversarial samples by using pseudo-labeled in-domain text data to train a seq2seq model for adversarial generation and combine it with paraphrase detection. We showcase the benefit of our approach for a real-world Natural Language Understanding (NLU) task, which maps a user`s request to an intent. Furthermore, we experiment with gradient-based training for the NLU task and try using token importance scores to guide the adversarial text generation. We show that our approach can generate realistic and relevant adversarial samples compared to other state-of-the-art adversarial training methods. Applying adversarial training using these generated samples helps the NLU model to recover up to 70{\%} of these types of errors and makes the model more robust, especially in the tail distribution in a large scale real world application.

null

10.18653/v1/2022.emnlp-industry.26

null

28,035

inproceedings

fuchs-etal-2022-yet

Is it out yet? Automatic Future Product Releases Extraction from Web Data

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.27/

Fuchs, Gilad and Ben-shaul, Ido and Mandelbrod, Matan

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

263--271

Identifying the release of new products and their predicted demand in advance is highly valuable for E-Commerce marketplaces and retailers. The information of an upcoming product release is used for inventory management, marketing campaigns and pre-order suggestions. Often, the announcement of an upcoming product release is widely available in multiple web pages such as blogs, chats or news articles. However, to the best of our knowledge, an automatic system to extract future product releases from web data has not been presented. In this work we describe an ML-powered multi-stage pipeline to automatically identify future product releases and rank their predicted demand from unstructured pages across the whole web. Our pipeline includes a novel Longformer-based model which uses a global attention mechanism guided by pre-calculated Named Entity Recognition predictions related to product releases. The model training data is based on a new corpus of 30K web pages manually annotated to identify future product releases. We made the dataset openly available at \url{https://doi.org/10.5281/zenodo.6894770}.

null

10.18653/v1/2022.emnlp-industry.27

null

28,036

inproceedings

lin-etal-2022-automatic-scene

Automatic Scene-based Topic Channel Construction System for {E}-Commerce

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.28/

Lin, Peng and Zou, Yanyan and Wu, Lingfei and Ma, Mian and Ding, Zhuoye and Long, Bo

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

272--284

Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. As manual construction of channels is time-consuming due to billions of products as well as dynamic and diverse customers' interests, it is necessary to leverage AI techniques to automatically construct channels for certain usage scenarios and even discover novel topics. To be specific, we first frame the channel construction task as a two-step problem, i.e., scene-based topic generation and product clustering, and propose an E-commerce Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated production, consisting of scene-based topic generation model for the e-commerce domain, product clustering on the basis of topic similarity, as well as quality control based on automatic model filtering and human screening. Extensive offline experiments and online A/B test validates the effectiveness of such a novel product form as well as the proposed system. In addition, we also introduce the experience of deploying the proposed system on a real-world e-commerce recommendation platform.

null

10.18653/v1/2022.emnlp-industry.28

null

28,037

inproceedings

tang-etal-2022-speechnet

{S}peech{N}et: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.29/

Tang, Raphael and Kumar, Karun and Yang, Gefei and Pandey, Akshat and Mao, Yajie and Belyaev, Vladislav and Emmadi, Madhuri and Murray, Craig and Ture, Ferhan and Lin, Jimmy

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

285--293

End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweight computation for inference. Of course, this impedes commercialization since most companies lack vast human and computational resources. In this paper, we explore training and deploying an ASR system in the label-scarce, compute-limited setting. To reduce human labor, we use a third-party ASR system as a weak supervision source, supplemented with labeling functions derived from implicit user feedback. To accelerate inference, we propose to route production-time queries across a pool of CUDA graphs of varying input lengths, the distribution of which best matches the traffic`s. Compared to our third-party ASR, we achieve a relative improvement in word-error rate of 8{\%} and a speedup of 600{\%}. Our system, called SpeechNet, currently serves 12 million queries per day on our voice-enabled smart television. To our knowledge, this is the first time a large-scale, Wav2vec-based deployment has been described in the academic literature.

null

10.18653/v1/2022.emnlp-industry.29

null

28,038

inproceedings

stowe-etal-2022-controlled

Controlled Language Generation for Language Learning Items

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.30/

Stowe, Kevin and Ghosh, Debanjan and Zhao, Mengxuan

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

294--305

This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24{\%}) and complexity (9{\%}) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.

null

10.18653/v1/2022.emnlp-industry.30

null

28,039

inproceedings

wang-etal-2022-improving-text

Improving Text-to-{SQL} Semantic Parsing with Fine-grained Query Understanding

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.31/

Wang, Jun and Ng, Patrick and Li, Alexander Hanbo and Jiang, Jiarong and Wang, Zhiguo and Xiang, Bing and Nallapati, Ramesh and Sengupta, Sudipta

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

306--312

Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance. In addition, without lexical-level fine-grained query understanding, linking between query and database can only rely on fuzzy string match which leads to suboptimal performance in real applications. In view of this, in this paper we present a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly modeling query and database, NER model analyzes user intents and identifies entities in the query. NEL model links typed entities to schema and cell values in database. Parser model leverages available semantic information and linking results and synthesizes tree-structured SQL queries based on dynamically generated grammar. Experiments on SQUALL, a newly released semantic parsing dataset, show that we can achieve 56.8{\%} execution accuracy on WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model by 2.7{\%}.

null

10.18653/v1/2022.emnlp-industry.31

null

28,040

inproceedings

li-etal-2022-unsupervised-dense

Unsupervised Dense Retrieval for Scientific Articles

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.32/

Li, Dan and Yadav, Vikrant and Afzal, Zubair and Tsatsaronis, George

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

313--321

In this work, we build a dense retrieval based semantic search engine on scientific articles from Elsevier. The major challenge is that there is no labeled data for training and testing. We apply a state-of-the-art unsupervised dense retrieval model called Generative Pseudo Labeling that generates high-quality pseudo training labels. Furthermore, since the articles are unbalanced across different domains, we select passages from multiple domains to form balanced training data. For the evaluation, we create two test sets: one manually annotated and one automatically created from the meta-information of our data. We compare the semantic search engine with the currently deployed lexical search engine on the two test sets. The results of the experiment show that the semantic search engine trained with pseudo training labels can significantly improve search performance.

null

10.18653/v1/2022.emnlp-industry.32

null

28,041

inproceedings

govind-sohoney-2022-learning

Learning Geolocations for Cold-Start and Hard-to-Resolve Addresses via Deep Metric Learning

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.33/

Govind and Sohoney, Saurabh

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

322--331

With evergrowing digital adoption in the society and increasing demand for businesses to deliver to customers doorstep, the last mile hop of transportation planning poses unique challenges in emerging geographies with unstructured addresses. One of the crucial inputs to facilitate effective planning is the task of geolocating customer addresses. Existing systems operate by aggregating historical delivery locations or by resolving/matching addresses to known buildings and campuses to vend a high-precision geolocation. However, by design they fail to cater to a significant fraction of addresses which are new in the system and have inaccurate or missing building level information. We propose a framework to resolve these addresses (referred to as hard-to-resolve henceforth) to a shallower granularity termed as neighbourhood. Specifically, we propose a weakly supervised deep metric learning model to encode the geospatial semantics in address embeddings. We present empirical evaluation on India (IN) and the United Arab Emirates (UAE) hard-to-resolve addresses to show significant improvements in learning geolocations i.e., 22{\%} (IN) {\&} 55{\%} (UAE) reduction in delivery defects (where learnt geocode is Y meters away from actual location), and 43{\%} (IN) {\&} 90{\%} (UAE) reduction in 50th percentile (p50) distance between learnt and actual delivery locations over the existing production system.

null

10.18653/v1/2022.emnlp-industry.33

null

28,042

inproceedings

sehanobish-etal-2022-meta

Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.34/

Sehanobish, Arijit and Kannan, Kawshik and Abraham, Nabila and Das, Anasuya and Odry, Benjamin

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

332--347

Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstream tasks becomes time consuming and expensive. In this work, we propose a simple extension of the Prototypical Networks for few-shot text classification. Our main idea is to replace the class prototypes by Gaussians and introduce a regularization term that encourages the examples to be clustered near the appropriate class centroids. Experimental results show that our method outperforms various strong baselines on 13 public and 4 internal datasets. Furthermore, we use the class distributions as a tool for detecting potential out-of-distribution (OOD) data points during deployment.

null

10.18653/v1/2022.emnlp-industry.34

null

28,043

inproceedings

koleva-etal-2022-named

Named Entity Recognition in Industrial Tables using Tabular Language Models

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.35/

Koleva, Aneta and Ringsquandl, Martin and Buckley, Mark and Hasan, Rakeb and Tresp, Volker

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

348--356

Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformers are still missing. In this paper, we study how these models can be applied to an industrial Named Entity Recognition (NER) problem where the entities are mentioned in tabular-structured spreadsheets. The highly technical nature of spreadsheets as well as the lack of labeled data present major challenges for fine-tuning transformer-based models. Therefore, we develop a dedicated table data augmentation strategy based on available domain-specific knowledge graphs. We show that this boosts performance in our low-resource scenario considerably. Further, we investigate the benefits of tabular structure as inductive bias compared to tables as linearized sequences. Our experiments confirm that a table transformer outperforms other baselines and that its tabular inductive bias is vital for convergence of transformer-based models.

null

10.18653/v1/2022.emnlp-industry.35

null

28,044

inproceedings

chen-etal-2022-reinforced

Reinforced Question Rewriting for Conversational Question Answering

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.36/

Chen, Zhiyu and Zhao, Jie and Fang, Anjie and Fetahu, Besnik and Rokhlenko, Oleg and Malmasi, Shervin

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

357--370

Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scratch. Previous work trains rewriting models using human rewrites as supervision. However, such objectives are disconnected with QA models and therefore more human-like rewrites do not guarantee better QA performance. In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. Experiments show that our approach can effectively improve QA performance over baselines for both extractive and retrieval QA. Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.

null

10.18653/v1/2022.emnlp-industry.36

null

28,045

inproceedings

schroedl-etal-2022-improving

Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.37/

Schroedl, Stefan and Kumar, Manoj and Hajebi, Kiana and Ziyadi, Morteza and Venkatapathy, Sriram and Ramakrishna, Anil and Gupta, Rahul and Natarajan, Pradeep

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

371--378

This paper presents an approach to identify samples from live traffic where the customer implicitly communicated satisfaction with Alexa`s responses, by leveraging interpretations of model behavior. Such customer signals are noisy and adding a large number of samples from live traffic to training set makes re-training infeasible. Our work addresses these challenges by identifying a small number of samples that grow training set by {\textasciitilde}0.05{\%} while producing statistically significant improvements in both offline and online tests.

null

10.18653/v1/2022.emnlp-industry.37

null

28,046

inproceedings

zhong-etal-2022-improving-precancerous

Improving Precancerous Case Characterization via Transformer-based Ensemble Learning

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.38/

Zhong, Yizhen and Xiao, Jiajie and Vetterli, Thomas and Matin, Mahan and Loo, Ellen and Lin, Jimmy and Bourgon, Richard and Shapira, Ofer

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

379--389

The application of natural language processing (NLP) to cancer pathology reports has been focused on detecting cancer cases, largely ignoring precancerous cases. Improving the characterization of precancerous adenomas assists in developing diagnostic tests for early cancer detection and prevention, especially for colorectal cancer (CRC). Here we developed transformer-based deep neural network NLP models to perform the CRC phenotyping, with the goal of extracting precancerous lesion attributes and distinguishing cancer and precancerous cases. We achieved 0.914 macro-F1 scores for classifying patients into negative, non-advanced adenoma, advanced adenoma and CRC. We further improved the performance to 0.923 using an ensemble of classifiers for cancer status classification and lesion size named-entity recognition (NER). Our results demonstrated the potential of using NLP to leverage real-world health record data to facilitate the development of diagnostic tests for early cancer prevention.

null

10.18653/v1/2022.emnlp-industry.38

null

28,047

inproceedings

chen-etal-2022-developing

Developing Prefix-Tuning Models for Hierarchical Text Classification

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.39/

Chen, Lei and Chou, Houwei and Zhu, Xiaodan

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

390--397

Hierarchical text classification (HTC) is a key problem and task in many industrial applications, which aims to predict labels organized in a hierarchy for given input text. For example, HTC can group the descriptions of online products into a taxonomy or organizing customer reviews into a hierarchy of categories. In real-life applications, while Pre-trained Language Models (PLMs) have dominated many NLP tasks, they face significant challenges too{---}the conventional fine-tuning process needs to modify and save models with a huge number of parameters. This is becoming more critical for HTC in both global and local modelling{---}the latter needs to learn multiple classifiers at different levels/nodes in a hierarchy. The concern will be even more serious since PLM sizes are continuing to increase in order to attain more competitive performances. Most recently, prefix tuning has become a very attractive technology by only tuning and saving a tiny set of parameters. Exploring prefix turning for HTC is hence highly desirable and has timely impact. In this paper, we investigate prefix tuning on HTC in two typical setups: local and global HTC. Our experiment shows that the prefix-tuning model only needs less than 1{\%} of parameters and can achieve performance comparable to regular full fine-tuning. We demonstrate that using contrastive learning in learning prefix vectors can further improve HTC performance.

null

10.18653/v1/2022.emnlp-industry.39

null

28,048

inproceedings

bis-etal-2022-paige

{PAIGE}: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.40/

Bi{\'s}, Daniel and Gupta, Saurabh and Hao, Jie and Fan, Xing and Guo, Chenlei

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

398--408

Unexpected responses or repeated clarification questions from conversational agents detract from the users' experience with technology meant to streamline their daily tasks. To reduce these frictions, Query Rewriting (QR) techniques replace transcripts of faulty queries with alternatives that lead to responses thatsatisfy the users' needs. Despite their successes, existing QR approaches are limited in their ability to fix queries that require considering users' personal preferences. We improve QR by proposing Personalized Adaptive Interactions Graph Encoder (PAIGE).PAIGE is the first QR architecture that jointly models user`s affinities and query semantics end-to-end. The core idea is to represent previous user-agent interactions and world knowledge in a structured form {---} a heterogeneous graph {---} and apply message passing to propagate latent representations of users' affinities to refine utterance embeddings.Using these embeddings, PAIGE can potentially provide different rewrites given the same query for users with different preferences. Our model, trained without any human-annotated data, improves the rewrite retrieval precision of state-of-the-art baselines by 12.5{--}17.5{\%} while having nearly ten times fewer parameters.

null

10.18653/v1/2022.emnlp-industry.40

null

28,049

inproceedings

gee-etal-2022-fast

Fast Vocabulary Transfer for Language Model Compression

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.41/

Gee, Leonidas and Zugarini, Andrea and Rigutini, Leonardo and Torroni, Paolo

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

409--416

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.

null

10.18653/v1/2022.emnlp-industry.41

null

28,050

inproceedings

wanigasekara-etal-2022-multimodal

Multimodal Context Carryover

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.42/

Wanigasekara, Prashan and Gupta, Nalin and Yang, Fan and Barut, Emre and Raeesy, Zeynab and Qin, Kechen and Rawls, Stephen and Liu, Xinyue and Su, Chengwei and Sandiri, Spurthi

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

417--428

Multi-modality support has become an integral part of creating a seamless user experience with modern voice assistants with smart displays. Users refer to images, video thumbnails, or the accompanying text descriptions on the screen through voice communication with AI powered devices. This raises the need to either augment existing commercial voice only dialogue systems with state-of-the-art multimodal components, or to introduce entirely new architectures; where the latter can lead to costly system revamps. To support the emerging visual navigation and visual product selection use cases, we propose to augment commercially deployed voice-only dialogue systems with additional multi-modal components. In this work, we present a novel yet pragmatic approach to expand an existing dialogue-based context carryover system (Chen et al., 2019a) in a voice assistant with state-of-the-art multimodal components to facilitate quick delivery of visual modality support with minimum changes. We demonstrate a 35{\%} accuracy improvement over the existing system on an in-house multi-modal visual navigation data set.

null

10.18653/v1/2022.emnlp-industry.42

null

28,051

inproceedings

fetahu-etal-2022-distilling

Distilling Multilingual Transformers into {CNN}s for Scalable Intent Classification

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.43/

Fetahu, Besnik and Veeragouni, Akash and Rokhlenko, Oleg and Malmasi, Shervin

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

429--439

We describe an application of Knowledge Distillation used to distill and deploy multilingual Transformer models for voice assistants, enabling text classification for customers globally. Transformers have set new state-of-the-art results for tasks like intent classification, and multilingual models exploit cross-lingual transfer to allow serving requests across 100+ languages. However, their prohibitive inference time makes them impractical to deploy in real-world scenarios with low latency requirements, such as is the case of voice assistants. We address the problem of cross-architecture distillation of multilingual Transformers to simpler models, while maintaining multilinguality without performance degradation. Training multilingual student models has received little attention, and is our main focus. We show that a teacher-student framework, where the teacher`s unscaled activations (logits) on unlabelled data are used to supervise student model training, enables distillation of Transformers into efficient multilingual CNN models. Our student model achieves equivalent performance as the teacher, and outperforms a similar model trained on the labelled data used to train the teacher model. This approach has enabled us to accurately serve global customer requests at speed (18x improvement), scale, and low cost.

null

10.18653/v1/2022.emnlp-industry.43

null

28,052

inproceedings

obadinma-etal-2022-bringing

Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.44/

Obadinma, Stephen and Khan Khattak, Faiza and Wang, Shirley and Sidhorn, Tania and Lau, Elaine and Robertson, Sean and Niu, Jingcheng and Au, Winnie and Munim, Alif and Kalaiselvi Bhaskar, Karthik Raja

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

440--450

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA`s core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}.

null

10.18653/v1/2022.emnlp-industry.44

null

28,053

inproceedings

el-kurdi-etal-2022-zero

Zero-Shot Dynamic Quantization for Transformer Inference

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.45/

El-kurdi, Yousef and Quinn, Jerry and Sil, Avi

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

451--457

We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure, or they require an additional calibration step to adjust parameters that also requires a selected held-out dataset. Our method permits taking advantage of quantization without the need for these adjustments. We present results on several NLP tasks demonstrating the usefulness of this technique.

null

10.18653/v1/2022.emnlp-industry.45

null

28,054

inproceedings

estes-etal-2022-fact

Fact Checking Machine Generated Text with Dependency Trees

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.46/

Estes, Alex and Vedula, Nikhita and Collins, Marcus and Cecil, Matt and Rokhlenko, Oleg

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

458--466

Factual and logical errors made by Natural Language Generation (NLG) systems limit their applicability in many settings. We study this problem in a conversational search and recommendation setting, and observe that we can often make two simplifying assumptions in this domain: (i) there exists a body of structured knowledge we can use for verifying factuality of generated text; and (ii) the text to be factually assessed typically has a well-defined structure and style. Grounded in these assumptions, we propose a fast, unsupervised and explainable technique, DepChecker, that assesses factuality of input text based on rules derived from structured knowledge patterns and dependency relations with respect to the input text. We show that DepChecker outperforms state-of-the-art, general purpose fact-checking techniques in this special, but important case.

null

10.18653/v1/2022.emnlp-industry.46

null

28,055

inproceedings

zalmout-li-2022-prototype

Prototype-Representations for Training Data Filtering in Weakly-Supervised Information Extraction

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.47/

Zalmout, Nasser and Li, Xian

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

467--474

The availability of high quality training data is still a bottleneck for the practical utilization of information extraction models, despite the breakthroughs in zero and few-shot learning techniques. This is further exacerbated for industry applications, where new tasks, domains, and specific use cases keep arising, which makes it impractical to depend on manually annotated data. Therefore, weak and distant supervision emerged as popular approaches to bootstrap training, utilizing labeling functions to guide the annotation process. Weakly-supervised annotation of training data is fast and efficient, however, it results in many irrelevant and out-of-context matches. This is a challenging problem that can degrade the performance in downstream models, or require a manual data cleaning step that can incur significant overhead. In this paper we present a prototype-based filtering approach, that can be utilized to denoise weakly supervised training data. The system is very simple, unsupervised, scalable, and requires little manual intervention, yet results in significant precision gains. We apply the technique in the task of attribute value extraction in e-commerce websites, and achieve up to 9{\%} gain in precision for the downstream models, with a minimal drop in recall.

null

10.18653/v1/2022.emnlp-industry.47

null

28,056

inproceedings

hao-etal-2022-cgf

{CGF}: Constrained Generation Framework for Query Rewriting in Conversational {AI}

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.48/

Hao, Jie and Liu, Yang and Fan, Xing and Gupta, Saurabh and Soltan, Saleh and Chada, Rakesh and Natarajan, Pradeep and Guo, Chenlei and Tur, Gokhan

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

475--483

In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users' accent or their abridged language. In this work, we present a novel Constrained Generation Framework (CGF) for query rewriting at both global and personalized levels. It is based on the encoder-decoder framework, where the encoder takes the query and its previous dialogue turns as the input to form a context-enhanced representation, and the decoder uses constrained decoding to generate the rewrites based on the pre-defined global or personalized constrained decoding space. Extensive offline and online A/B experiments show that the proposed CGF significantly boosts the query rewriting performance.

null

10.18653/v1/2022.emnlp-industry.48

null

28,057

inproceedings

fu-etal-2022-entity

Entity-level Sentiment Analysis in Contact Center Telephone Conversations

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.49/

Fu, Xue-yong and Chen, Cheng and Laskar, Md Tahmid Rahman and Gardiner, Shayna and Hiranandani, Pooja and Tn, Shashi Bhushan

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

484--491

Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a neural network supplemented with some heuristic rules.

null

10.18653/v1/2022.emnlp-industry.49

null

28,058

inproceedings

srinivasan-etal-2022-quill

{QUILL}: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.50/

Srinivasan, Krishna and Raman, Karthik and Samanta, Anupam and Liao, Lingrui and Bertelli, Luca and Bendersky, Michael

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

492--501

Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lack of nuance or context. Complicated feature engineering efforts do not always lead to downstream improvements as their performance benefits may be offset by increased complexity of knowledge distillation. Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding. While Retrieval Augmentation typically increases latency of LMs (thus hurting distillation efficacy), (2) we provide a practical and effective way of distilling Retrieval Augmentation LLMs. Specifically, we use a novel two-stage distillation approach that allows us to carry over the gains of retrieval augmentation, without suffering the increased compute typically associated with it. (3) We demonstrate the benefits of the proposed approach (QUILL) on a billion-scale, real-world query understanding system resulting in huge gains. Via extensive experiments, including on public benchmarks, we believe this work offers a recipe for practical use of retrieval-augmented query understanding.

null

10.18653/v1/2022.emnlp-industry.50

null

28,059

inproceedings

qian-etal-2022-distinguish

Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.51/

Qian, Cheng and Qi, Haode and Wang, Gengyu and Kunc, Ladislav and Potdar, Saloni

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

502--511

Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query. Accurately tagging a query as out-of-domain is particularly hard in scenarios when the chatbot is not equipped to handle a topic which has semantic overlap with an existing topic it is trained on. We propose a simple yet effective OOS detection method that outperforms standard OOS detection methods in a real-world deployment of virtual assistants. We discuss the various design and deployment considerations for a cloud platform solution to train virtual assistants and deploy them at scale. Additionally, we propose a collection of datasets that replicates real-world scenarios and show comprehensive results in various settings using both offline and online evaluation metrics.

null

10.18653/v1/2022.emnlp-industry.51

null

28,060

inproceedings

lei-etal-2022-plato

{PLATO}-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.52/

Lei, Zeyang and Zhang, Chao and Xu, Xinchao and Wu, Wenquan and Niu, Zheng-yu and Wu, Hua and Wang, Haifeng and Yang, Yi and Li, Shuanglong

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

512--520

Online advertisement text generation aims at generating attractive and persuasive text ads to appeal to users clicking ads or purchasing products. While pretraining-based models have achieved remarkable success in generating high-quality text ads, some challenges still remain, such as ad generation in low-resource scenarios and training efficiency for multiple ad tasks. In this paper, we propose a novel unified text ad generation framework with multi-task prompt learning, called PLATO-Ad, totackle these problems. Specifically, we design a three-phase transfer learning mechanism to tackle the low-resource ad generation problem. Furthermore, we present a novel multi-task prompt learning mechanism to efficiently utilize a single lightweight model to solve multiple ad generation tasks without loss of performance compared to training a separate model for each task. Finally, we conduct offline and online evaluations and experiment results show that PLATO-Ad significantly outperforms the state-of-the-art on both offline and online metrics. PLATO-Ad has been deployed in a leading advertising platform with 3.5{\%} CTR improvement on search ad descriptions and 10.4{\%} CTR improvement on feed ad titles.

null

10.18653/v1/2022.emnlp-industry.52

null

28,061

inproceedings

gupta-etal-2022-dense

Dense Feature Memory Augmented Transformers for {COVID}-19 Vaccination Search Classification

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.53/

Gupta, Jai and Tay, Yi and Kamath, Chaitanya and Tran, Vinh and Metzler, Donald and Bavadekar, Shailesh and Sun, Mimi and Gabrilovich, Evgeniy

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

521--530

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15{\%} improvement in F1 score and +14{\%} in precision.

null

10.18653/v1/2022.emnlp-industry.53

null

28,062

inproceedings

park-lee-2022-full

Full-Stack Information Extraction System for Cybersecurity Intelligence

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.54/

Park, Youngja and Lee, Taesung

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

531--539

Due to rapidly growing cyber-attacks and security vulnerabilities, many reports on cyber-threat intelligence (CTI) are being published daily. While these reports can help security analysts to understand on-going cyber threats,the overwhelming amount of information makes it difficult to digest the information in a timely manner. This paper presents, SecIE, an industrial-strength full-stack information extraction (IE) system for the security domain. SecIE can extract a large number of security entities, relations and the temporal information of the relations, which is critical for cyberthreat investigations. Our evaluation with 133 labeled threat reports containing 108,021 tokens shows thatSecIE achieves over 92{\%} F1-score for entity extraction and about 70{\%} F1-score for relation extraction. We also showcase how SecIE can be used for downstream security applications.

null

10.18653/v1/2022.emnlp-industry.54

null

28,063

inproceedings

nayak-garera-2022-deploying

Deploying Unified {BERT} Moderation Model for {E}-Commerce Reviews

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.55/

Nayak, Ravindra and Garera, Nikesh

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

540--547

Moderation of user-generated e-commerce content has become crucial due to the large and diverse user base on the platforms. Product reviews and ratings have become an integral part of the shopping experience to build trust among users. Due to the high volume of reviews generated on a vast catalog of products, manual moderation is infeasible, making machine moderation a necessity. In this work, we described our deployed system and models for automated moderation of user-generated content. At the heart of our approach, we outline several rejection reasons for review {\&} rating moderation and explore a unified BERT model to moderate them. We convey the importance of product vertical embeddings for the relevancy of the review for a given product and highlight the advantages of pre-training the BERT models with monolingual data to cope with the domain gap in the absence of huge labelled datasets. We observe a 4.78{\%} F1 increase with less labelled data and a 2.57{\%} increase in F1 score on the review data compared to the publicly available BERT-based models. Our best model In-House-BERT-vertical sends only 5.89{\%} of total reviews to manual moderation and has been deployed in production serving live traffic for millions of users.

null

10.18653/v1/2022.emnlp-industry.55

null

28,064

inproceedings

zhou-etal-2022-simans

{S}im{ANS}: Simple Ambiguous Negatives Sampling for Dense Text Retrieval

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.56/

Zhou, Kun and Gong, Yeyun and Liu, Xiao and Zhao, Wayne Xin and Shen, Yelong and Dong, Anlei and Lu, Jingwen and Majumder, Rangan and Wen, Ji-rong and Duan, Nan

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

548--559

Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninformative or false negative problem. In this work, we empirically show that according to the measured relevance scores, the negatives ranked around the positives are generally more informative and less likely to be false negatives. Intuitively, these negatives are not too hard (\textit{may be false negatives}) or too easy (\textit{uninformative}). They are the ambiguous negatives and need more attention during training.Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.Extensive experiments on four public and one industry datasets show the effectiveness of our approach.We made the code and models publicly available in \url{https://github.com/microsoft/SimXNS}.

null

10.18653/v1/2022.emnlp-industry.56

null

28,065

inproceedings

zhang-etal-2022-revisiting

Revisiting and Advancing {C}hinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.57/

Zhang, Taolin and Dong, Junwei and Wang, Jianing and Wang, Chengyu and Wang, Ang and Liu, Yinghui and Huang, Jun and Li, Yong and He, Xiaofeng

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

560--570

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge bases, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community to support various language understanding applications. In this paper, we revisit and advance the development of Chinese natural language understanding with a series of novel Chinese KEPLMs released in various parameter sizes, namely CKBERT (Chinese knowledge-enhanced BERT). Specifically, both relational and linguistic knowledge is effectively injected into CKBERT based on two novel pre-training tasks, i.e., linguistic-aware masked language modeling and contrastive multi-hop relation modeling. Based on the above two pre-training paradigms and our in-house implemented TorchAccelerator, we have pre-trained base (110M), large (345M) and huge (1.3B) versions of CKBERT efficiently on GPU clusters. Experiments demonstrate that CKBERT consistently outperforms strong baselines for Chinese over various benchmark NLP tasks and in terms of different model sizes.

null

10.18653/v1/2022.emnlp-industry.57

null

28,066

inproceedings

oikawa-etal-2022-stacking

A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.58/

Oikawa, Yuto and Nakayama, Yuki and Murakami, Koji

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

571--578

In a live streaming chat on a video streaming service, it is crucial to filter out toxic comments with online processing to prevent users from reading comments in real-time. However, recent toxic language detection methods rely on deep learning methods, which can not be scalable considering inference speed. Also, these methods do not consider constraints of computational resources expected depending on a deployed system (e.g., no GPU resource).This paper presents an efficient method for toxic language detection that is aware of real-world scenarios. Our proposed architecture is based on partial stacking that feeds initial results with low confidence to meta-classifier. Experimental results show that our method achieves a much faster inference speed than BERT-based models with comparable performance.

null

10.18653/v1/2022.emnlp-industry.58

null

28,067

inproceedings

goyal-etal-2022-end

End-to-End Speech to Intent Prediction to improve {E}-commerce Customer Support Voicebot in {H}indi and {E}nglish

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.59/

Goyal, Abhinav and Singh, Anupam and Garera, Nikesh

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

579--586

Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have higher latency, and have complex deployment. These pipelines are also prone to compounding errors. To overcome these challenges, we discuss an end-to-end (E2E) S2I model for customer support voicebot task in a bilingual setting. We show how we can solve E2E intent classification by leveraging a pre-trained automatic speech recognition (ASR) model with slight modification and fine-tuning on small annotated datasets. Experimental results show that our best E2E model outperforms a conventional pipeline by a relative {\textasciitilde}27{\%} on the F1 score.

null

10.18653/v1/2022.emnlp-industry.59

null

28,068

inproceedings

cai-etal-2022-pile

{PILE}: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.60/

Cai, Lianshang and Zhang, Linhao and Ma, Dehong and Fan, Jun and Shi, Daiting and Wu, Yi and Cheng, Zhicong and Gu, Simiu and Yin, Dawei

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

587--595

Pre-trained language models have become a crucial part of ranking systems and achieved very impressive effects recently. To maintain high performance while keeping efficient computations, knowledge distillation is widely used. In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process. We propose a unified algorithm called Pairwise Iterative Logits Ensemble (PILE) to tackle these two questions simultaneously. PILE ensembles multi-teacher logits supervised by label information in an iterative way and achieved competitive performance in both offline and online experiments. The proposed method has been deployed in a real-world commercial search system.

null

10.18653/v1/2022.emnlp-industry.60

null

28,069

inproceedings

tutubalina-etal-2022-comprehensive

A Comprehensive Evaluation of Biomedical Entity-centric Search

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.61/

Tutubalina, Elena and Miftahutdinov, Zulfat and Muravlev, Vladimir and Shneyderman, Anastasia

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

596--605

Biomedical information retrieval has often been studied as a task of detecting whether a system correctly detects entity spans and links these entities to concepts from a given terminology. Most academic research has focused on evaluation of named entity recognition (NER) and entity linking (EL) models which are key components to recognizing diseases and genes in PubMed abstracts. In this work, we perform a fine-grained evaluation intended to understand the efficiency of state-of-the-art BERT-based information extraction (IE) architecture as a biomedical search engine. We present a novel manually annotated dataset of abstracts for disease and gene search. The dataset contains 23K query-abstract pairs, where 152 queries are selected from logs of our target discovery platform and PubMed abstracts annotated with relevance judgments. Specifically, the query list also includes a subset of concepts with at least one ambiguous concept name. As a baseline, we use off-she-shelf Elasticsearch with BM25. Our experiments on NER, EL, and retrieval in a zero-shot setup show the neural IE architecture shows superior performance for both disease and gene concept queries.

null

10.18653/v1/2022.emnlp-industry.61

null

28,070

inproceedings

morishita-etal-2022-domain

Domain Adaptation of Machine Translation with Crowdworkers

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.62/

Morishita, Makoto and Suzuki, Jun and Nagata, Masaaki

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

606--618

Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This situation restricts the applicability of machine translation when the target domain`s data are limited. However, there is great demand for high-quality domain-specific machine translation models for many domains. We propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers.With the collected parallel data, we can quickly adapt a machine translation model to the target domain. Our experiments show that the proposed method can collect target-domain parallel data over a few days at a reasonable cost. We tested it with five domains, and the domain-adapted model improved the BLEU scores to +19.7 by an average of +7.8 points compared to a general-purpose translation model.

null

10.18653/v1/2022.emnlp-industry.62

null

28,071

inproceedings

yoon-etal-2022-biomedical

Biomedical {NER} for the Enterprise with Distillated {BERN}2 and the Kazu Framework

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.63/

Yoon, Wonjin and Jackson, Richard and Ford, Elliot and Poroshin, Vladimir and Kang, Jaewoo

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

619--626

In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora. Decades of study of the field of BioNLP has produced a plethora of algorithms, systems and datasets. However, our experience has been that no single open source system meets all the requirements of a modern pharmaceutical company. In this work, we describe these requirements according to our experience of the industry, and present Kazu, a highly extensible, scalable open source framework designed to support BioNLP for the pharmaceutical sector. Kazu is a built around a computationally efficient version of the BERN2 NER model (TinyBERN2), and subsequently wraps several other BioNLP technologies into one coherent system.

null

10.18653/v1/2022.emnlp-industry.63

null

28,072

inproceedings

patil-garera-2022-large

Large-scale Machine Translation for {I}ndian Languages in {E}-commerce under Low Resource Constraints

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.64/

Patil, Amey and Garera, Nikesh

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

627--634

The democratization of e-commerce platforms has moved an increasingly diversified Indian user base to shop online. We have deployed reliable and precise large-scale Machine Translation systems for several Indian regional languages in this work. Building such systems is a challenge because of the low-resource nature of the Indian languages. We develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. We show strong synthetic parallel data generation capability and consistent improvements to the model over iterations. Starting with 1.2M parallel pairs for English-Hindi we have compiled a corpus with 400M+ synthetic high quality parallel pairs across different domains. Further, we need colloquial translations to preserve the intent and friendliness of English content in regional languages, and make it easier to understand for our users. We perform robust and effective domain adaptation steps to achieve colloquial such translations. Over iterations, we show 9.02 BLEU points improvement for English to Hindi translation model. Along with Hindi, we show that the overall approach and best practices extends well to other Indian languages, resulting in deployment of our models across 7 Indian Languages.

null

10.18653/v1/2022.emnlp-industry.64

null

28,073

inproceedings

eklund-forsman-2022-topic

Topic Modeling by Clustering Language Model Embeddings: Human Validation on an Industry Dataset

Li, Yunyao and Lazaridou, Angeliki

dec

2022

Abu Dhabi, UAE

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-industry.65/

Eklund, Anton and Forsman, Mona

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

635--643

Topic models are powerful tools to get an overview of large collections of text data, a situation that is prevalent in industry applications. A rising trend within topic modeling is to directly cluster dimension-reduced embeddings created with pretrained language models. It is difficult to evaluate these models because there is no ground truth and automatic measurements may not mimic human judgment. To address this problem, we created a tool called STELLAR for interactive topic browsing which we used for human evaluation of topics created from a real-world dataset used in industry. Embeddings created with BERT were used together with UMAP and HDBSCAN to model the topics. The human evaluation found that our topic model creates coherent topics. The following discussion revolves around the requirements of industry and what research is needed for production-ready systems.

null

10.18653/v1/2022.emnlp-industry.65

null

28,074

inproceedings

mohanty-2022-deftri

{DEFT}ri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.1/

Mohanty, Ipsita

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

1--7

Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for automated defect triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels fused text embeddings to improve contextual representations from human-generated product defects. For our multi-label text classification defect triage task, we also introduce a Walmart proprietary dataset of product defects using weak supervision and adversarial learning, in a few-shot setting.

null

10.18653/v1/2022.ecnlp-1.1

null

28,076

inproceedings

wang-etal-2022-interactive

Interactive Latent Knowledge Selection for {E}-Commerce Product Copywriting Generation

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.2/

Wang, Zeming and Zou, Yanyan and Fang, Yuejian and Chen, Hongshen and Ma, Mian and Ding, Zhuoye and Long, Bo

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

8--19

As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product`s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase. Automatically generating product copywriting has attracted noticeable interests from both academic and industrial communities, where existing solutions merely make use of a product`s title and attribute information to generate its corresponding description. However, in addition to the product title and attributes, we observe that there are various auxiliary descriptions created by the shoppers or marketers in the e-commerce platforms (namely human knowledge), which contains valuable information for product copywriting generation, yet always accompanying lots of noises. In this work, we propose a novel solution to automatically generating product copywriting that involves all the title, attributes and denoised auxiliary knowledge. To be specific, we design an end-to-end generation framework equipped with two variational autoencoders that works interactively to select informative human knowledge and generate diverse copywriting.

null

10.18653/v1/2022.ecnlp-1.2

null

28,077

inproceedings

liu-etal-2022-leveraging

Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.3/

Liu, Yang and Chordia, Varnith and Li, Hua and Fazeli Dehkordy, Siavash and Sun, Yifei and Gao, Vincent and Zhang, Na

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

20--28

In a leading e-commerce business, we receive hundreds of millions of customer feedback from different text communication channels such as product reviews. The feedback can contain rich information regarding customers' dissatisfaction in the quality of goods and services. To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text. We identify issues at two levels: coarse grained (L-Coarse) and fine grained (L-Granular). We formulate this multi-level product issue identification problem as a seq2seq language generation problem. Specifically, we utilize transformer-based seq2seq models due to their versatility and strong transfer-learning capability. We demonstrate that our approach is label efficient and outperforms the traditional approach such as multi-class multi-label classification formulation. Based on human evaluation, our fine-tuned model achieves 82.1{\%} and 95.4{\%} human-level performance for L-Coarse and L-Granular issue identification, respectively. Furthermore, our experiments illustrate that the model can generalize to identify unseen L-Granular issues.

null

10.18653/v1/2022.ecnlp-1.3

null

28,078

inproceedings

kondadadi-etal-2022-data

Data Quality Estimation Framework for Faster Tax Code Classification

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.4/

Kondadadi, Ravi and Williams, Allen and Nicolov, Nicolas

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

29--34

This paper describes a novel framework to estimate the data quality of a collection of product descriptions to identify required relevant information for accurate product listing classification for tax-code assignment. Our Data Quality Estimation (DQE) framework consists of a Question Answering (QA) based attribute value extraction model to identify missing attributes and a classification model to identify bad quality records. We show that our framework can accurately predict the quality of product descriptions. In addition to identifying low-quality product listings, our framework can also generate a detailed report at a category level showing missing product information resulting in a better customer experience.

null

10.18653/v1/2022.ecnlp-1.4

null

28,079

inproceedings

dong-etal-2022-cml

{CML}: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.5/

Dong, Bo and Wang, Yiyi and Sun, Hanbo and Wang, Yunji and Hashemi, Alireza and Du, Zheng

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

35--43

Deep neural network models are especially susceptible to noise in annotated labels. In the real world, annotated data typically contains noise caused by a variety of factors such as task difficulty, annotator experience, and annotator bias. Label quality is critical for label validation tasks; however, correcting for noise by collecting more data is often costly. In this paper, we propose a contrastive meta-learning framework (CML) to address the challenges introduced by noisy annotated data, specifically in the context of natural language processing. CML combines contrastive and meta learning to improve the quality of text feature representations. Meta-learning is also used to generate confidence scores to assess label quality. We demonstrate that a model built on CML-filtered data outperforms a model built on clean data. Furthermore, we perform experiments on deidentified commercial voice assistant datasets and demonstrate that our model outperforms several SOTA approaches.

null

10.18653/v1/2022.ecnlp-1.5

null

28,080

inproceedings

bagheri-garakani-etal-2022-improving

Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.6/

Bagheri Garakani, Alireza and Yang, Fan and Hua, Wen-Yu and Chen, Yetian and Momma, Michinari and Deng, Jingyuan and Gao, Yan and Sun, Yi

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

44--48

Ensuring relevance quality in product search is a critical task as it impacts the customer`s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.

null

10.18653/v1/2022.ecnlp-1.6

null

28,081

inproceedings

jain-etal-2022-comparative

Comparative Snippet Generation

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.7/

Jain, Saurabh and Miao, Yisong and Kan, Min-Yen

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

49--57

We model products' reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and an analysis of performance of a pre-trained BERT model to generate such snippets.

null

10.18653/v1/2022.ecnlp-1.7

null

28,082

inproceedings

shido-etal-2022-textual

Textual Content Moderation in {C}2{C} Marketplace

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.8/

Shido, Yusuke and Liu, Hsien-Chi and Umezawa, Keisuke

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

58--62

Automatic monitoring systems for inappropriate user-generated messages have been found to be effective in reducing human operation costs in Consumer to Consumer (C2C) marketplace services, in which customers send messages directly to other customers. We propose a lightweight neural network that takes a conversation as input, which we deployed to a production service. Our results show that the system reduced the human operation costs to less than one-sixth compared to the conventional rule-based monitoring at Mercari.

null

10.18653/v1/2022.ecnlp-1.8

null

28,083

inproceedings

yang-etal-2022-spelling

Spelling Correction using Phonetics in {E}-commerce Search

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.9/

Yang, Fan and Bagheri Garakani, Alireza and Teng, Yifei and Gao, Yan and Liu, Jia and Deng, Jingyuan and Sun, Yi

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

63--67

In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., {\textquotedblleft}bluetooth sound system{\textquotedblright} vs. {\textquotedblleft}blutut sant sistam{\textquotedblright}) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9{\%}+ and recall of phonetic errors by 8{\%}+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.

null

10.18653/v1/2022.ecnlp-1.9

null

28,084

inproceedings

beygi-etal-2022-logical

Logical Reasoning for Task Oriented Dialogue Systems

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.10/

Beygi, Sajjad and Fazel-Zarandi, Maryam and Cervone, Alessandra and Krishnan, Prakash and Jonnalagadda, Siddhartha

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

68--79

In recent years, large pretrained models have been used in dialogue systems to improve successful task completion rates. However, lack of reasoning capabilities of dialogue platforms make it difficult to provide relevant and fluent responses, unless the designers of a conversational experience spend a considerable amount of time implementing these capabilities in external rule based modules. In this work, we propose a novel method to fine-tune pretrained transformer models such as Roberta and T5, to reason over a set of facts in a given dialogue context. Our method includes a synthetic data generation mechanism which helps the model learn logical relations, such as comparison between list of numerical values, inverse relations (and negation), inclusion and exclusion for categorical attributes, and application of a combination of attributes over both numerical and categorical values, and spoken form for numerical values, without need for additional training data. We show that the transformer based model can perform logical reasoning to answer questions when the dialogue context contains all the required information, otherwise it is able to extract appropriate constraints to pass to downstream components (e.g. a knowledge base) when partial information is available. We observe that transformer based models such as UnifiedQA-T5 can be fine-tuned to perform logical reasoning (such as numerical and categorical attributes' comparison) over attributes seen at training time (e.g., accuracy of 90{\%}+ for comparison of smaller than kmax=5 values over heldout test dataset).

null

10.18653/v1/2022.ecnlp-1.10

null

28,085

inproceedings

kumar-etal-2022-cova

{C}o{VA}: Context-aware Visual Attention for Webpage Information Extraction

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.11/

Kumar, Anurendra and Morabia, Keval and Wang, William and Chang, Kevin and Schwing, Alex

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

80--90

Webpage information extraction (WIE) is an important step to create knowledge bases. For this, classical WIE methods leverage the Document Object Model (DOM) tree of a website. However, use of the DOM tree poses significant challenges as context and appearance are encoded in an abstract manner. To address this challenge we propose to reformulate WIE as a context-aware Webpage Object Detection task. Specifically, we develop a Context-aware Visual Attention-based (CoVA) detection pipeline which combines appearance features with syntactical structure from the DOM tree. To study the approach we collect a new large-scale datase of e-commerce websites for which we manually annotate every web element with four labels: product price, product title, product image and others. On this dataset we show that the proposed CoVA approach is a new challenging baseline which improves upon prior state-of-the-art methods.

null

10.18653/v1/2022.ecnlp-1.11

null

28,086

inproceedings

fuchs-acriche-2022-product

Product Titles-to-Attributes As a Text-to-Text Task

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.12/

Fuchs, Gilad and Acriche, Yoni

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

91--98

Online marketplaces use attribute-value pairs, such as brand, size, size type, color, etc. to help define important and relevant facts about a listing. These help buyers to curate their search results using attribute filtering and overall create a richer experience. Although their critical importance for listings' discoverability, getting sellers to input tens of different attribute-value pairs per listing is costly and often results in missing information. This can later translate to the unnecessary removal of relevant listings from the search results when buyers are filtering by attribute values. In this paper we demonstrate using a Text-to-Text hierarchical multi-label ranking model framework to predict the most relevant attributes per listing, along with their expected values, using historic user behavioral data. This solution helps sellers by allowing them to focus on verifying information on attributes that are likely to be used by buyers, and thus, increase the expected recall for their listings. Specifically for eBay`s case we show that using this model can improve the relevancy of the attribute extraction process by 33.2{\%} compared to the current highly-optimized production system. Apart from the empirical contribution, the highly generalized nature of the framework presented in this paper makes it relevant for many high-volume search-driven websites.

null

10.18653/v1/2022.ecnlp-1.12

null

28,087

inproceedings

shen-etal-2022-product

Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.13/

Shen, Xiaoyu and Barlacchi, Gianni and Del Tredici, Marco and Cheng, Weiwei and Byrne, Bill and Gispert, Adri{\`a}

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

99--110

It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semi-structured attributes, text descriptions, user-provided contents, etc. However, these sources have different structures and writing styles, which poses challenges for (1) evidence ranking, (2) source selection, and (3) answer generation. In this paper, we build a benchmark with annotations for both evidence selection and answer generation covering 6 information sources. Based on this benchmark, we conduct a comprehensive study and present a set of best practices. We show that all sources are important and contribute to answering questions. Handling all sources within one single model can produce comparable confidence scores across sources and combining multiple sources for training always helps, even for sources with totally different structures. We further propose a novel data augmentation method to iteratively create training samples for answer generation, which achieves close-to-human performance with only a few thousandannotations. Finally, we perform an in-depth error analysis of model predictions and highlight the challenges for future research.

null

10.18653/v1/2022.ecnlp-1.13

null

28,088

inproceedings

shen-etal-2022-semipqa

semi{PQA}: A Study on Product Question Answering over Semi-structured Data

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.14/

Shen, Xiaoyu and Barlacchi, Gianni and Del Tredici, Marco and Cheng, Weiwei and Gispert, Adri{\`a}

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

111--120

Product question answering (PQA) aims to automatically address customer questions to improve their online shopping experience. Current research mainly focuses on finding answers from either unstructured text, like product descriptions and user reviews, or structured knowledge bases with pre-defined schemas. Apart from the above two sources, a lot of product information is represented in a semi-structured way, e.g., key-value pairs, lists, tables, json and xml files, etc. These semi-structured data can be a valuable answer source since they are better organized than free text, while being easier to construct than structured knowledge bases. However, little attention has been paid to them. To fill in this blank, here we study how to effectively incorporate semi-structured answer sources for PQA and focus on presenting answers in a natural, fluent sentence. To this end, we present semiPQA: a dataset to benchmark PQA over semi-structured data. It contains 11,243 written questions about json-formatted data covering 320 unique attribute types. Each data point is paired with manually-annotated text that describes its contents, so that we can train a neural answer presenter to present the data in a natural way. We provide baseline results and a deep analysis on the successes and challenges of leveraging semi-structured data for PQA. In general, state-of-the-art neural models can perform remarkably well when dealing with seen attribute types. For unseen attribute types, however, a noticeable drop is observed for both answer presentation and attribute ranking.

null

10.18653/v1/2022.ecnlp-1.14

null

28,089

inproceedings

kew-volk-2022-improving

Improving Specificity in Review Response Generation with Data-Driven Data Filtering

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.15/

Kew, Tannon and Volk, Martin

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

121--133

Responding to online customer reviews has become an essential part of successfully managing and growing a business both in e-commerce and the hospitality and tourism sectors. Recently, neural text generation methods intended to assist authors in composing responses have been shown to deliver highly fluent and natural looking texts. However, they also tend to learn a strong, undesirable bias towards generating overly generic, one-size-fits-all outputs to a wide range of inputs. While this often results in {\textquoteleft}safe', high-probability responses, there are many practical settings in which greater specificity is preferable. In this work we examine the task of generating more specific responses for online reviews in the hospitality domain by identifying generic responses in the training data, filtering them and fine-tuning the generation model. We experiment with a range of data-driven filtering methods and show through automatic and human evaluation that, despite a 60{\%} reduction in the amount of training data, filtering helps to derive models that are capable of generating more specific, useful responses.

null

10.18653/v1/2022.ecnlp-1.15

null

28,090

inproceedings

chen-etal-2022-extreme

Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.16/

Chen, Wei-Te and Xia, Yandi and Shinzato, Keiji

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

134--140

Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicalization of extracted values. Furthermore, since values needed for actual services is static in many attributes, extraction of new values is not always necessary. Given the above, we formalize AVE as extreme multi-label classification (XMC). A major problem in solving AVE as XMC is that the distribution between positive and negative labels for products is heavily imbalanced. To mitigate the negative impact derived from such biased distribution, we propose label masking, a simple and effective method to reduce the number of negative labels in training. We exploit attribute taxonomy designed for e-commerce platforms to determine which labels are negative for products. Experimental results using a dataset collected from a Japanese e-commerce platform demonstrate that the label masking improves micro and macro F$_1$ scores by 3.38 and 23.20 points, respectively.

null

10.18653/v1/2022.ecnlp-1.16

null

28,091

inproceedings

zhu-etal-2022-enhanced

Enhanced Representation with Contrastive Loss for Long-Tail Query Classification in e-commerce

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.17/

Zhu, Lvxing and Chen, Hao and Wei, Chao and Zhang, Weiru

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

141--150

Query classification is a fundamental task in an e-commerce search engine, which assigns one or multiple predefined product categories in response to each search query. Taking click-through logs as training data in deep learning methods is a common and effective approach for query classification. However, the frequency distribution of queries typically has long-tail property, which means that there are few logs for most of the queries. The lack of reliable user feedback information results in worse performance of long-tail queries compared with frequent queries. To solve the above problem, we propose a novel method that leverages an auxiliary module to enhance the representations of long-tail queries by taking advantage of reliable supervised information of variant frequent queries. The long-tail queries are guided by the contrastive loss to obtain category-aligned representations in the auxiliary module, where the variant frequent queries serve as anchors in the representation space. We train our model with real-world click data from AliExpress and conduct evaluation on both offline labeled data and online AB test. The results and further analysis demonstrate the effectiveness of our proposed method.

null

10.18653/v1/2022.ecnlp-1.17

null

28,092

inproceedings

howell-etal-2022-domain

Domain-specific knowledge distillation yields smaller and better models for conversational commerce

Malmasi, Shervin and Rokhlenko, Oleg and Ueffing, Nicola and Guy, Ido and Agichtein, Eugene and Kallumadi, Surya

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.ecnlp-1.18/

Howell, Kristen and Wang, Jian and Hazare, Akshay and Bradley, Joseph and Brew, Chris and Chen, Xi and Dunn, Matthew and Hockey, Beth and Maurer, Andrew and Widdows, Dominic

Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

151--160

We demonstrate that knowledge distillation can be used not only to reduce model size, but to simultaneously adapt a contextual language model to a specific domain. We use Multilingual BERT (mBERT; Devlin et al., 2019) as a starting point and follow the knowledge distillation approach of (Sahn et al., 2019) to train a smaller multilingual BERT model that is adapted to the domain at hand. We show that for in-domain tasks, the domain-specific model shows on average 2.3{\%} improvement in F1 score, relative to a model distilled on domain-general data. Whereas much previous work with BERT has fine-tuned the encoder weights during task training, we show that the model improvements from distillation on in-domain data persist even when the encoder weights are frozen during task training, allowing a single encoder to support classifiers for multiple tasks and languages.

null

10.18653/v1/2022.ecnlp-1.18

null

28,093