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

inproceedings

hewlett-etal-2017-accurate

Accurate Supervised and Semi-Supervised Machine Reading for Long Documents

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1214/

Hewlett, Daniel and Jones, Llion and Lacoste, Alexandre and Gur, Izzeddin

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2011--2020

We introduce a hierarchical architecture for machine reading capable of extracting precise information from long documents. The model divides the document into small, overlapping windows and encodes all windows in parallel with an RNN. It then attends over these window encodings, reducing them to a single encoding, which is decoded into an answer using a sequence decoder. This hierarchical approach allows the model to scale to longer documents without increasing the number of sequential steps. In a supervised setting, our model achieves state of the art accuracy of 76.8 on the WikiReading dataset. We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows. We evaluate models that can reuse autoencoder states and outputs without fine-tuning their weights, allowing for more efficient training and inference.

null

10.18653/v1/D17-1214

null

57,702

inproceedings

jia-liang-2017-adversarial

Adversarial Examples for Evaluating Reading Comprehension Systems

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1215/

Jia, Robin and Liang, Percy

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2021--2031

Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75{\%} F1 score to 36{\%}; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7{\%}. We hope our insights will motivate the development of new models that understand language more precisely.

null

10.18653/v1/D17-1215

null

57,703

inproceedings

lin-etal-2017-reasoning

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1216/

Lin, Hongyu and Sun, Le and Han, Xianpei

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2032--2043

Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.

null

10.18653/v1/D17-1216

null

57,704

inproceedings

yin-etal-2017-document

Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1217/

Yin, Yichun and Song, Yangqiu and Zhang, Ming

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2044--2054

Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo question-answer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines. We will release our code and data for the method replicability.

null

10.18653/v1/D17-1217

null

57,705

inproceedings

daxenberger-etal-2017-essence

What is the Essence of a Claim? Cross-Domain Claim Identification

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1218/

Daxenberger, Johannes and Eger, Steffen and Habernal, Ivan and Stab, Christian and Gurevych, Iryna

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2055--2066

Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent conceptualization of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.

null

10.18653/v1/D17-1218

null

57,706

inproceedings

du-cardie-2017-identifying

Identifying Where to Focus in Reading Comprehension for Neural Question Generation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1219/

Du, Xinya and Cardie, Claire

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2067--2073

A first step in the task of automatically generating questions for testing reading comprehension is to identify \textit{question-worthy} sentences, i.e. sentences in a text passage that humans find it worthwhile to ask questions about. We propose a hierarchical neural sentence-level sequence tagging model for this task, which existing approaches to question generation have ignored. The approach is fully data-driven {---} with no sophisticated NLP pipelines or any hand-crafted rules/features {---} and compares favorably to a number of baselines when evaluated on the SQuAD data set. When incorporated into an existing neural question generation system, the resulting end-to-end system achieves state-of-the-art performance for paragraph-level question generation for reading comprehension.

null

10.18653/v1/D17-1219

null

57,707

inproceedings

sterckx-etal-2017-break

Break it Down for Me: A Study in Automated Lyric Annotation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1220/

Sterckx, Lucas and Naradowsky, Jason and Byrne, Bill and Demeester, Thomas and Develder, Chris

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2074--2080

Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.

null

10.18653/v1/D17-1220

null

57,708

inproceedings

li-etal-2017-cascaded

Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1221/

Li, Piji and Lam, Wai and Bing, Lidong and Guo, Weiwei and Li, Hang

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2081--2090

When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience. By adding sparsity constraints on the number of output vectors, we can generate condensed information which can be treated as word salience. Fine-grained and coarse-grained sentence compression strategies are incorporated to produce compressive summaries. Experiments on some benchmark data sets show that our framework achieves better results than the state-of-the-art methods.

null

10.18653/v1/D17-1221

null

57,709

inproceedings

li-etal-2017-deep

Deep Recurrent Generative Decoder for Abstractive Text Summarization

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1222/

Li, Piji and Lam, Wai and Bing, Lidong and Wang, Zihao

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2091--2100

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.

null

10.18653/v1/D17-1222

null

57,710

inproceedings

isonuma-etal-2017-extractive

Extractive Summarization Using Multi-Task Learning with Document Classification

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1223/

Isonuma, Masaru and Fujino, Toru and Mori, Junichiro and Matsuo, Yutaka and Sakata, Ichiro

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2101--2110

The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems.

null

10.18653/v1/D17-1223

null

57,711

inproceedings

zhang-wan-2017-towards

Towards Automatic Construction of News Overview Articles by News Synthesis

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1224/

Zhang, Jianmin and Wan, Xiaojun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2111--2116

In this paper we investigate a new task of automatically constructing an overview article from a given set of news articles about a news event. We propose a news synthesis approach to address this task based on passage segmentation, ranking, selection and merging. Our proposed approach is compared with several typical multi-document summarization methods on the Wikinews dataset, and achieves the best performance on both automatic evaluation and manual evaluation.

null

10.18653/v1/D17-1224

null

57,712

inproceedings

zhao-huang-2017-joint

Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1225/

Zhao, Kai and Huang, Liang

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2117--2123

Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem rely on annotated text segmentations (Elementary Discourse Units, EDUs) and sophisticated sparse or continuous features to extract syntactic information. In this paper we propose the first end-to-end discourse parser that jointly parses in both syntax and discourse levels, as well as the first syntacto-discourse treebank by integrating the Penn Treebank and the RST Treebank. Built upon our recent span-based constituency parser, this joint syntacto-discourse parser requires no preprocessing efforts such as segmentation or feature extraction, making discourse parsing more convenient. Empirically, our parser achieves the state-of-the-art end-to-end discourse parsing accuracy.

null

10.18653/v1/D17-1225

null

57,713

inproceedings

choubey-huang-2017-event

Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1226/

Choubey, Prafulla Kumar and Huang, Ruihong

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2124--2133

We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.

null

10.18653/v1/D17-1226

null

57,714

inproceedings

huang-etal-2017-finish

When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1227/

Huang, Liang and Zhao, Kai and Ma, Mingbo

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2134--2139

In neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality. However, in the neural generation setting, hypotheses can finish in different steps, which makes it difficult to decide when to end beam search to ensure optimality. We propose a provably optimal beam search algorithm that will always return the optimal-score complete hypothesis (modulo beam size), and finish as soon as the optimality is established. To counter neural generation`s tendency for shorter hypotheses, we also introduce a bounded length reward mechanism which allows a modified version of our beam search algorithm to remain optimal. Experiments on neural machine translation demonstrate that our principled beam search algorithm leads to improvement in BLEU score over previously proposed alternatives.

null

10.18653/v1/D17-1227

null

57,715

inproceedings

wang-etal-2017-steering

Steering Output Style and Topic in Neural Response Generation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1228/

Wang, Di and Jojic, Nebojsa and Brockett, Chris and Nyberg, Eric

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2140--2150

We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selective-sampling. We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction. Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.

null

10.18653/v1/D17-1228

null

57,716

inproceedings

tran-etal-2017-preserving

Preserving Distributional Information in Dialogue Act Classification

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1229/

Tran, Quan Hung and Zukerman, Ingrid and Haffari, Gholamreza

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2151--2156

This paper introduces a novel training/decoding strategy for sequence labeling. Instead of greedily choosing a label at each time step, and using it for the next prediction, we retain the probability distribution over the current label, and pass this distribution to the next prediction. This approach allows us to avoid the effect of label bias and error propagation in sequence learning/decoding. Our experiments on dialogue act classification demonstrate the effectiveness of this approach. Even though our underlying neural network model is relatively simple, it outperforms more complex neural models, achieving state-of-the-art results on the MapTask and Switchboard corpora.

null

10.18653/v1/D17-1229

null

57,717

inproceedings

li-etal-2017-adversarial

Adversarial Learning for Neural Dialogue Generation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1230/

Li, Jiwei and Monroe, Will and Shi, Tianlin and Jean, S{\'e}bastien and Ritter, Alan and Jurafsky, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2157--2169

We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator{---}analagous to the human evaluator in the Turing test{---} to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines

null

10.18653/v1/D17-1230

null

57,718

inproceedings

liu-etal-2017-using-context

Using Context Information for Dialog Act Classification in {DNN} Framework

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1231/

Liu, Yang and Han, Kun and Tan, Zhao and Lei, Yun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2170--2178

Previous work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent studies explored using deep learning neural networks for DA classification, however, it is not clear yet what is the best method for using dialog context or DA sequential information, and how much gain it brings. This paper proposes several ways of using context information for DA classification, all in the deep learning framework. The baseline system classifies each utterance using the convolutional neural networks (CNN). Our proposed methods include using hierarchical models (recurrent neural networks (RNN) or CNN) for DA sequence tagging where the bottom layer takes the sentence CNN representation as input, concatenating predictions from the previous utterances with the CNN vector for classification, and performing sequence decoding based on the predictions from the sentence CNN model. We conduct thorough experiments and comparisons on the Switchboard corpus, demonstrate that incorporating context information significantly improves DA classification, and show that we achieve new state-of-the-art performance for this task.

null

10.18653/v1/D17-1231

null

57,719

inproceedings

jo-etal-2017-modeling

Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1232/

Jo, Yohan and Yoder, Michael and Jang, Hyeju and Ros{\'e}, Carolyn

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2179--2189

We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.

null

10.18653/v1/D17-1232

null

57,720

inproceedings

yao-etal-2017-towards

Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1233/

Yao, Lili and Zhang, Yaoyuan and Feng, Yansong and Zhao, Dongyan and Yan, Rui

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2190--2199

The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.

null

10.18653/v1/D17-1233

null

57,721

inproceedings

chang-etal-2017-affordable

Affordable On-line Dialogue Policy Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1234/

Chang, Cheng and Yang, Runzhe and Chen, Lu and Zhou, Xiang and Yu, Kai

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2200--2209

The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.

null

10.18653/v1/D17-1234

null

57,722

inproceedings

shao-etal-2017-generating

Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1235/

Shao, Yuanlong and Gouws, Stephan and Britz, Denny and Goldie, Anna and Strope, Brian and Kurzweil, Ray

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2210--2219

Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation of long, informative, coherent, and diverse responses remains a hard task. In this work, we focus on the single turn setting. We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.

null

10.18653/v1/D17-1235

null

57,723

inproceedings

eshghi-etal-2017-bootstrapping

Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1236/

Eshghi, Arash and Shalyminov, Igor and Lemon, Oliver

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2220--2230

We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74{\%} of the Facebook AI bAbI dataset even when trained on only 0.13{\%} of the data (5 dialogues). It can in addition process 65{\%} of bAbI+, a corpus we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-the-art retrieval model, MEMN2N. We find that, in terms of semantic accuracy, the MEMN2N model shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.

null

10.18653/v1/D17-1236

null

57,724

inproceedings

peng-etal-2017-composite

Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1237/

Peng, Baolin and Li, Xiujun and Li, Lihong and Gao, Jianfeng and Celikyilmaz, Asli and Lee, Sungjin and Wong, Kam-Fai

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2231--2240

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of: (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over three baselines, two based on handcrafted rules and the other based on flat deep reinforcement learning.

null

10.18653/v1/D17-1237

null

57,725

inproceedings

novikova-etal-2017-need

Why We Need New Evaluation Metrics for {NLG}

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1238/

Novikova, Jekaterina and Du{\v{s}}ek, Ond{\v{r}}ej and Cercas Curry, Amanda and Rieser, Verena

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2241--2252

The majority of NLG evaluation relies on automatic metrics, such as BLEU . In this paper, we motivate the need for novel, system- and data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is data- and system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.

null

10.18653/v1/D17-1238

null

57,726

inproceedings

wiseman-etal-2017-challenges

Challenges in Data-to-Document Generation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1239/

Wiseman, Sam and Shieber, Stuart and Rush, Alexander

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2253--2263

Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate human-generated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copy- and reconstruction-based extensions lead to noticeable improvements.

null

10.18653/v1/D17-1239

null

57,727

inproceedings

patro-etal-2017-english

All that is {E}nglish may be {H}indi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1240/

Patro, Jasabanta and Samanta, Bidisha and Singh, Saurabh and Basu, Abhipsa and Mukherjee, Prithwish and Choudhury, Monojit and Mukherjee, Animesh

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2264--2274

n this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman`s correlation values, our methods perform more than two times better ({\ensuremath{\sim}} 0.62) in predicting the borrowing likeliness compared to the best performing baseline ({\ensuremath{\sim}} 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88{\%} of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.

null

10.18653/v1/D17-1240

null

57,728

inproceedings

ding-etal-2017-multi

Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1241/

Ding, Tao and Bickel, Warren K. and Pan, Shimei

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2275--2284

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different unsupervised feature learning methods to take advantage of a large amount of unsupervised social media data. We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook {\textquotedblleft}likes{\textquotedblright} and {\textquotedblleft}status updates{\textquotedblright} to enhance system performance. Based on our evaluation, our best models achieved 86{\%} AUC for predicting tobacco use, 81{\%} for alcohol use and 84{\%} for illicit drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user`s social media behavior (e.g., word usage) and substance use.

null

10.18653/v1/D17-1241

null

57,729

inproceedings

garimella-etal-2017-demographic

Demographic-aware word associations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1242/

Garimella, Aparna and Banea, Carmen and Mihalcea, Rada

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2285--2295

Variations of word associations across different groups of people can provide insights into people`s psychologies and their world views. To capture these variations, we introduce the task of demographic-aware word associations. We build a new gold standard dataset consisting of word association responses for approximately 300 stimulus words, collected from more than 800 respondents of different gender (male/female) and from different locations (India/United States), and show that there are significant variations in the word associations made by these groups. We also introduce a new demographic-aware word association model based on a neural net skip-gram architecture, and show how computational methods for measuring word associations that specifically account for writer demographics can outperform generic methods that are agnostic to such information.

null

10.18653/v1/D17-1242

null

57,730

inproceedings

cheng-etal-2017-factored

A Factored Neural Network Model for Characterizing Online Discussions in Vector Space

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1243/

Cheng, Hao and Fang, Hao and Ostendorf, Mari

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2296--2306

We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.

null

10.18653/v1/D17-1243

null

57,731

inproceedings

rashid-blanco-2017-dimensions

Dimensions of Interpersonal Relationships: Corpus and Experiments

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1244/

Rashid, Farzana and Blanco, Eduardo

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2307--2316

This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.

null

10.18653/v1/D17-1244

null

57,732

inproceedings

dusmanu-etal-2017-argument

Argument Mining on {T}witter: Arguments, Facts and Sources

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1245/

Dusmanu, Mihai and Cabrio, Elena and Villata, Serena

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2317--2322

Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.

null

10.18653/v1/D17-1245

null

57,733

inproceedings

aoki-etal-2017-distinguishing

Distinguishing {J}apanese Non-standard Usages from Standard Ones

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1246/

Aoki, Tatsuya and Sasano, Ryohei and Takamura, Hiroya and Okumura, Manabu

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2323--2328

We focus on non-standard usages of common words on social media. In the context of social media, words sometimes have other usages that are totally different from their original. In this study, we attempt to distinguish non-standard usages on social media from standard ones in an unsupervised manner. Our basic idea is that non-standardness can be measured by the inconsistency between the expected meaning of the target word and the given context. For this purpose, we use context embeddings derived from word embeddings. Our experimental results show that the model leveraging the context embedding outperforms other methods and provide us with findings, for example, on how to construct context embeddings and which corpus to use.

null

10.18653/v1/D17-1246

null

57,734

inproceedings

sap-etal-2017-connotation

Connotation Frames of Power and Agency in Modern Films

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1247/

Sap, Maarten and Prasettio, Marcella Cindy and Holtzman, Ari and Rashkin, Hannah and Choi, Yejin

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2329--2334

The framing of an action influences how we perceive its actor. We introduce connotation frames of power and agency, a pragmatic formalism organized using frame semantic representations, to model how different levels of power and agency are implicitly projected on actors through their actions. We use the new power and agency frames to measure the subtle, but prevalent, gender bias in the portrayal of modern film characters and provide insights that deviate from the well-known Bechdel test. Our contributions include an extended lexicon of connotation frames along with a web interface that provides a comprehensive analysis through the lens of connotation frames.

null

10.18653/v1/D17-1247

null

57,735

inproceedings

preotiuc-pietro-etal-2017-controlling

Controlling Human Perception of Basic User Traits

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1248/

Preo{\c{t}}iuc-Pietro, Daniel and Chandra Guntuku, Sharath and Ungar, Lyle

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2335--2341

Much of our online communication is text-mediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information {--} gender and age {--} is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random {--} an over 20{\%} drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.

null

10.18653/v1/D17-1248

null

57,736

inproceedings

gautrais-etal-2017-topic

Topic Signatures in Political Campaign Speeches

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1249/

Gautrais, Cl{\'e}ment and Cellier, Peggy and Quiniou, Ren{\'e} and Termier, Alexandre

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2342--2347

Highlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign. In this paper, we present a method combining standard topic modeling with signature mining for analyzing topic recurrence in speeches of Clinton and Trump during the 2016 American presidential campaign. The results show that the method extracts automatically the main ideas of each candidate and, in addition, provides information about the evolution of these topics during the campaign.

null

10.18653/v1/D17-1249

null

57,737

inproceedings

schwartz-etal-2017-assessing

Assessing Objective Recommendation Quality through Political Forecasting

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1250/

Schwartz, H. Andrew and Rouhizadeh, Masoud and Bishop, Michael and Tetlock, Philip and Mellers, Barbara and Ungar, Lyle

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2348--2357

Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility. We explore recommendation quality assessment with respect to both subjective (i.e. users' ratings) and objective (i.e., did it influence? did it improve decisions?) metrics in a massive online geopolitical forecasting system, ultimately comparing linguistic characteristics of each quality metric. Using a variety of features, we predict all types of quality with better accuracy than the simple yet strong baseline of comment length. Looking at the most predictive content illustrates rater biases; for example, forecasters are subjectively biased in favor of comments mentioning business transactions or dealings as well as material things, even though such comments do not indeed prove any more useful objectively. Additionally, more complex sentence constructions, as evidenced by subordinate conjunctions, are characteristic of comments leading to objective improvements in forecasting.

null

10.18653/v1/D17-1250

null

57,738

inproceedings

shirakawa-etal-2017-never

Never Abandon Minorities: Exhaustive Extraction of Bursty Phrases on Microblogs Using Set Cover Problem

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1251/

Shirakawa, Masumi and Hara, Takahiro and Maekawa, Takuya

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2358--2367

We propose a language-independent data-driven method to exhaustively extract bursty phrases of arbitrary forms (e.g., phrases other than simple noun phrases) from microblogs. The burst (i.e., the rapid increase of the occurrence) of a phrase causes the burst of overlapping N-grams including incomplete ones. In other words, bursty incomplete N-grams inevitably overlap bursty phrases. Thus, the proposed method performs the extraction of bursty phrases as the set cover problem in which all bursty N-grams are covered by a minimum set of bursty phrases. Experimental results using Japanese Twitter data showed that the proposed method outperformed word-based, noun phrase-based, and segmentation-based methods both in terms of accuracy and coverage.

null

10.18653/v1/D17-1251

null

57,739

inproceedings

peng-etal-2017-maximum

Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1252/

Peng, Haoruo and Chang, Ming-Wei and Yih, Wen-tau

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2368--2378

Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural network-based framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.

null

10.18653/v1/D17-1252

null

57,740

inproceedings

wachsmuth-etal-2017-impact

The Impact of Modeling Overall Argumentation with Tree Kernels

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1253/

Wachsmuth, Henning and Da San Martino, Giovanni and Kiesel, Dora and Stein, Benno

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2379--2389

Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure. So far, the adequacy of these models of overall argumentation remains unclear. This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation. We analyze patterns in the overall argumentation of texts from three corpora. Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time. In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure. Our results suggest that either of them is necessary while their combination may be beneficial.

null

10.18653/v1/D17-1253

null

57,741

inproceedings

gan-etal-2017-learning

Learning Generic Sentence Representations Using Convolutional Neural Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1254/

Gan, Zhe and Pu, Yunchen and Henao, Ricardo and Li, Chunyuan and He, Xiaodong and Carin, Lawrence

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2390--2400

We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoder-decoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.

null

10.18653/v1/D17-1254

null

57,742

inproceedings

amiri-etal-2017-repeat

Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1255/

Amiri, Hadi and Miller, Timothy and Savova, Guergana

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2401--2410

We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their {\textquotedblleft}reviews{\textquotedblright} are spaced over time. Our algorithm uses only 34-50{\%} of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available at \url{scholar.harvard.edu/hadi/RbF/}.

null

10.18653/v1/D17-1255

null

57,743

inproceedings

gui-etal-2017-part

Part-of-Speech Tagging for {T}witter with Adversarial Neural Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1256/

Gui, Tao and Zhang, Qi and Huang, Haoran and Peng, Minlong and Huang, Xuanjing

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2411--2420

In this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled in-domain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.

null

10.18653/v1/D17-1256

null

57,744

inproceedings

li-etal-2017-investigating

Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1257/

Li, Bofang and Liu, Tao and Zhao, Zhe and Tang, Buzhou and Drozd, Aleksandr and Rogers, Anna and Du, Xiaoyong

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2421--2431

The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.

null

10.18653/v1/D17-1257

null

57,745

inproceedings

braud-etal-2017-syntax

Does syntax help discourse segmentation? Not so much

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1258/

Braud, Chlo{\'e} and Lacroix, Oph{\'e}lie and S{\o}gaard, Anders

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2432--2442

Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token boundaries. We therefore investigate to what extent constituents can be replaced with universal dependencies, or left out completely, as well as how state-of-the-art segmenters fare in the absence of sentence boundaries. Our results show that dependency information is less useful than expected, but we provide a fully scalable, robust model that only relies on part-of-speech information, and show that it performs well across languages in the absence of any gold-standard annotation.

null

10.18653/v1/D17-1258

null

57,746

inproceedings

lewis-etal-2017-deal

Deal or No Deal? End-to-End Learning of Negotiation Dialogues

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1259/

Lewis, Mike and Yarats, Denis and Dauphin, Yann and Parikh, Devi and Batra, Dhruv

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2443--2453

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other`s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.

null

10.18653/v1/D17-1259

null

57,747

inproceedings

chen-etal-2017-agent

Agent-Aware Dropout {DQN} for Safe and Efficient On-line Dialogue Policy Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1260/

Chen, Lu and Zhou, Xiang and Chang, Cheng and Yang, Runzhe and Yu, Kai

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2454--2464

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a \textit{companion learning} framework to integrate the two approaches for \textit{on-line} dialogue policy learning, in which a pre-defined rule-based policy acts as a {\textquotedblleft}teacher{\textquotedblright} and guides a data-driven RL system by giving example actions as well as additional rewards. A novel \textit{agent-aware dropout} Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher`s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both \textit{safety}and \textit{efficiency} of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.

null

10.18653/v1/D17-1260

null

57,748

inproceedings

potash-rumshisky-2017-towards

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1261/

Potash, Peter and Rumshisky, Anna

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2465--2475

In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.

null

10.18653/v1/D17-1261

null

57,749

inproceedings

ma-etal-2017-investigation

Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1262/

Ma, Qingsong and Graham, Yvette and Baldwin, Timothy and Liu, Qun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2476--2485

Monolingual evaluation of Machine Translation (MT) aims to simplify human assessment by requiring assessors to compare the meaning of the MT output with a reference translation, opening up the task to a much larger pool of genuinely qualified evaluators. Monolingual evaluation runs the risk, however, of bias in favour of MT systems that happen to produce translations superficially similar to the reference and, consistent with this intuition, previous investigations have concluded monolingual assessment to be strongly biased in this respect. On re-examination of past analyses, we identify a series of potential analytical errors that force some important questions to be raised about the reliability of past conclusions, however. We subsequently carry out further investigation into reference bias via direct human assessment of MT adequacy via quality controlled crowd-sourcing. Contrary to both intuition and past conclusions, results for show no significant evidence of reference bias in monolingual evaluation of MT.

null

10.18653/v1/D17-1262

null

57,750

inproceedings

isabelle-etal-2017-challenge

A Challenge Set Approach to Evaluating Machine Translation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1263/

Isabelle, Pierre and Cherry, Colin and Foster, George

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2486--2496

Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system`s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.

null

10.18653/v1/D17-1263

null

57,751

inproceedings

nakashole-flauger-2017-knowledge

Knowledge Distillation for Bilingual Dictionary Induction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1264/

Nakashole, Ndapandula and Flauger, Raphael

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2497--2506

Leveraging zero-shot learning to learn mapping functions between vector spaces of different languages is a promising approach to bilingual dictionary induction. However, methods using this approach have not yet achieved high accuracy on the task. In this paper, we propose a bridging approach, where our main contribution is a knowledge distillation training objective. As teachers, rich resource translation paths are exploited in this role. And as learners, translation paths involving low resource languages learn from the teachers. Our training objective allows seamless addition of teacher translation paths for any given low resource pair. Since our approach relies on the quality of monolingual word embeddings, we also propose to enhance vector representations of both the source and target language with linguistic information. Our experiments on various languages show large performance gains from our distillation training objective, obtaining as high as 17{\%} accuracy improvements.

null

10.18653/v1/D17-1264

null

57,752

inproceedings

bawden-2017-machine

Machine Translation, it`s a question of style, innit? The case of {E}nglish tag questions

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1265/

Bawden, Rachel

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2507--2512

In this paper, we address the problem of generating English tag questions (TQs) (e.g. it is, isn`t it?) in Machine Translation (MT). We propose a post-edition solution, formulating the problem as a multi-class classification task. We present (i) the automatic annotation of English TQs in a parallel corpus of subtitles and (ii) an approach using a series of classifiers to predict TQ forms, which we use to post-edit state-of-the-art MT outputs. Our method provides significant improvements in English TQ translation when translating from Czech, French and German, in turn improving the fluidity, naturalness, grammatical correctness and pragmatic coherence of MT output.

null

10.18653/v1/D17-1265

null

57,753

inproceedings

pourdamghani-knight-2017-deciphering

Deciphering Related Languages

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1266/

Pourdamghani, Nima and Knight, Kevin

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2513--2518

We present a method for translating texts between close language pairs. The method does not require parallel data, and it does not require the languages to be written in the same script. We show results for six language pairs: Afrikaans/Dutch, Bosnian/Serbian, Danish/Swedish, Macedonian/Bulgarian, Malaysian/Indonesian, and Polish/Belorussian. We report BLEU scores showing our method to outperform others that do not use parallel data.

null

10.18653/v1/D17-1266

null

57,754

inproceedings

st-arnaud-etal-2017-identifying

Identifying Cognate Sets Across Dictionaries of Related Languages

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1267/

St Arnaud, Adam and Beck, David and Kondrak, Grzegorz

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2519--2528

We present a system for identifying cognate sets across dictionaries of related languages. The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences. The similarity scores are used to cluster words from different languages that may originate from a common proto-word. When tested on the Algonquian language family, our system detects 63{\%} of cognate sets while maintaining cluster purity of 70{\%}.

null

10.18653/v1/D17-1267

null

57,755

inproceedings

malaviya-etal-2017-learning

Learning Language Representations for Typology Prediction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1268/

Malaviya, Chaitanya and Neubig, Graham and Littell, Patrick

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2529--2535

One central mystery of neural NLP is what neural models {\textquotedblleft}know{\textquotedblright} about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one NMT system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages geographic and phylogenetic neighbors.

null

10.18653/v1/D17-1268

null

57,756

inproceedings

mayhew-etal-2017-cheap

Cheap Translation for Cross-Lingual Named Entity Recognition

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1269/

Mayhew, Stephen and Tsai, Chen-Tse and Roth, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2536--2545

Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with \textit{very} minimal resources. Our approach makes use of a lexicon to {\textquotedblleft}translate{\textquotedblright} annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5{\%} F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.

null

10.18653/v1/D17-1269

null

57,757

inproceedings

vulic-etal-2017-cross

Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1270/

Vuli{\'c}, Ivan and Mrk{\v{s}}i{\'c}, Nikola and Korhonen, Anna

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2546--2558

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.

null

10.18653/v1/D17-1270

null

57,758

inproceedings

friedrich-gateva-2017-classification

Classification of telicity using cross-linguistic annotation projection

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1271/

Friedrich, Annemarie and Gateva, Damyana

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2559--2565

This paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint ({\textquotedblleft}she walked home{\textquotedblright}), while an atelic event does not ({\textquotedblleft}she walked around{\textquotedblright}). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb`s meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity.

null

10.18653/v1/D17-1271

null

57,759

inproceedings

lawrence-etal-2017-counterfactual

Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1272/

Lawrence, Carolin and Sokolov, Artem and Riezler, Stefan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2566--2576

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system. A challenge arises by the fact that risk-averse commercial SMT systems deterministically log the most probable translation. The lack of sufficient exploration of the SMT output space seemingly contradicts the theoretical requirements for counterfactual learning. We show that counterfactual learning from deterministic bandit logs is possible nevertheless by smoothing out deterministic components in learning. This can be achieved by additive and multiplicative control variates that avoid degenerate behavior in empirical risk minimization. Our simulation experiments show improvements of up to 2 BLEU points by counterfactual learning from deterministic bandit feedback.

null

10.18653/v1/D17-1272

null

57,760

inproceedings

wang-etal-2017-learning-fine

Learning Fine-grained Relations from {C}hinese User Generated Categories

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1273/

Wang, Chengyu and Fan, Yan and He, Xiaofeng and Zhou, Aoying

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2577--2587

User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.

null

10.18653/v1/D17-1273

null

57,761

inproceedings

huang-etal-2017-improving

Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1274/

Huang, Lifu and Sil, Avirup and Ji, Heng and Florian, Radu

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2588--2597

Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities{\_}of{\_}residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF with the following new strategies: (1). Take a regularized dependency graph instead of a raw sentence as input to DNN, to compress the wide contexts between query and candidate filler; (2). Incorporate two attention mechanisms: local attention learned from query and candidate filler, and global attention learned from external knowledge bases, to guide the model to better select indicative contexts to determine slot type. Experiments show that this framework outperforms state-of-the-art on both relation extraction (16{\%} absolute F-score gain) and slot filling validation for each individual system (up to 8.5{\%} absolute F-score gain).

null

10.18653/v1/D17-1274

null

57,762

inproceedings

durrett-etal-2017-identifying

Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1275/

Durrett, Greg and Kummerfeld, Jonathan K. and Berg-Kirkpatrick, Taylor and Portnoff, Rebecca and Afroz, Sadia and McCoy, Damon and Levchenko, Kirill and Paxson, Vern

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2598--2607

One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own {\textquotedblleft}fine-grained domain{\textquotedblright} in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.

null

10.18653/v1/D17-1275

null

57,763

inproceedings

muis-lu-2017-labeling

Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1276/

Muis, Aldrian Obaja and Lu, Wei

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2608--2618

In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.

null

10.18653/v1/D17-1276

null

57,764

inproceedings

ganea-hofmann-2017-deep

Deep Joint Entity Disambiguation with Local Neural Attention

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1277/

Ganea, Octavian-Eugen and Hofmann, Thomas

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2619--2629

We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.

null

10.18653/v1/D17-1277

null

57,765

inproceedings

gashteovski-etal-2017-minie

{M}in{IE}: Minimizing Facts in Open Information Extraction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1278/

Gashteovski, Kiril and Gemulla, Rainer and del Corro, Luciano

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2630--2640

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner. In this paper, we propose MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.

null

10.18653/v1/D17-1278

null

57,766

inproceedings

luan-etal-2017-scientific

Scientific Information Extraction with Semi-supervised Neural Tagging

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1279/

Luan, Yi and Ostendorf, Mari and Hajishirzi, Hannaneh

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2641--2651

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.

null

10.18653/v1/D17-1279

null

57,767

inproceedings

tang-etal-2017-nite

{NITE}: A Neural Inductive Teaching Framework for Domain Specific {NER}

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1280/

Tang, Siliang and Zhang, Ning and Zhang, Jinjiang and Wu, Fei and Zhuang, Yueting

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2652--2657

In domain-specific NER, due to insufficient labeled training data, deep models usually fail to behave normally. In this paper, we proposed a novel Neural Inductive TEaching framework (NITE) to transfer knowledge from existing domain-specific NER models into an arbitrary deep neural network in a teacher-student training manner. NITE is a general framework that builds upon transfer learning and multiple instance learning, which collaboratively not only transfers knowledge to a deep student network but also reduces the noise from teachers. NITE can help deep learning methods to effectively utilize existing resources (i.e., models, labeled and unlabeled data) in a small domain. The experiment resulted on Disease NER proved that without using any labeled data, NITE can significantly boost the performance of a CNN-bidirectional LSTM-CRF NER neural network nearly over 30{\%} in terms of F1-score.

null

10.18653/v1/D17-1280

null

57,768

inproceedings

sharma-etal-2017-speeding

Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1281/

Sharma, Aditya and Parekh, Zarana and Talukdar, Partha

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2658--2663

RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process. RLIE-DQN trains a single agent sequentially, training on one instance at a time. This results in significant training slowdown which is undesirable. We leverage recent advances in parallel RL training using asynchronous methods and propose RLIE-A3C. RLIE-A3C trains multiple agents in parallel and is able to achieve upto 6x training speedup over RLIE-DQN, while suffering no loss in average accuracy.

null

10.18653/v1/D17-1281

null

57,769

inproceedings

li-etal-2017-leveraging

Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1282/

Li, Peng-Hsuan and Dong, Ruo-Ping and Wang, Yu-Siang and Chou, Ju-Chieh and Ma, Wei-Yun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2664--2669

In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition. In contrast to classical sequential labeling methods, the system first identifies which text chunks are possible named entities by whether they are linguistic constituents. Then it classifies these chunks with a constituency tree structure by recursively propagating syntactic and semantic information to each constituent node. This method surpasses current state-of-the-art on OntoNotes 5.0 with automatically generated parses.

null

10.18653/v1/D17-1282

null

57,770

inproceedings

strubell-etal-2017-fast

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1283/

Strubell, Emma and Verga, Patrick and Belanger, David and McCallum, Andrew

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2670--2680

Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds.

null

10.18653/v1/D17-1283

null

57,771

inproceedings

gupta-etal-2017-entity

Entity Linking via Joint Encoding of Types, Descriptions, and Context

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1284/

Gupta, Nitish and Singh, Sameer and Roth, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2681--2690

For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively {\textquotedblleft}embed{\textquotedblright} entities that are new to the KB, and is able to link its mentions accurately.

null

10.18653/v1/D17-1284

null

57,772

inproceedings

he-etal-2017-insight

An Insight Extraction System on {B}io{M}edical Literature with Deep Neural Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1285/

He, Hua and Ganjam, Kris and Jain, Navendu and Lundin, Jessica and White, Ryen and Lin, Jimmy

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2691--2701

Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.

null

10.18653/v1/D17-1285

null

57,773

inproceedings

nastase-strapparava-2017-word

Word Etymology as Native Language Interference

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1286/

Nastase, Vivi and Strapparava, Carlo

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2702--2707

We present experiments that show the influence of native language on lexical choice when producing text in another language {--} in this particular case English. We start from the premise that non-native English speakers will choose lexical items that are close to words in their native language. This leads us to an etymology-based representation of documents written by people whose mother tongue is an Indo-European language. Based on this representation we grow a language family tree, that matches closely the Indo-European language tree.

null

10.18653/v1/D17-1286

null

57,774

inproceedings

eisenberg-finlayson-2017-simpler

A Simpler and More Generalizable Story Detector using Verb and Character Features

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1287/

Eisenberg, Joshua and Finlayson, Mark

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2708--2715

Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14{\%} improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000{\%}) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features{--}ones related to events, and ones related to characters{--}totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance.

null

10.18653/v1/D17-1287

null

57,775

inproceedings

schulz-kuhn-2017-multi

Multi-modular domain-tailored {OCR} post-correction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1288/

Schulz, Sarah and Kuhn, Jonas

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2716--2726

One of the main obstacles for many Digital Humanities projects is the low data availability. Texts have to be digitized in an expensive and time consuming process whereas Optical Character Recognition (OCR) post-correction is one of the time-critical factors. At the example of OCR post-correction, we show the adaptation of a generic system to solve a specific problem with little data. The system accounts for a diversity of errors encountered in OCRed texts coming from different time periods in the domain of literature. We show that the combination of different approaches, such as e.g. Statistical Machine Translation and spell checking, with the help of a ranking mechanism tremendously improves over single-handed approaches. Since we consider the accessibility of the resulting tool as a crucial part of Digital Humanities collaborations, we describe the workflow we suggest for efficient text recognition and subsequent automatic and manual post-correction

null

10.18653/v1/D17-1288

null

57,776

inproceedings

luo-etal-2017-learning

Learning to Predict Charges for Criminal Cases with Legal Basis

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1289/

Luo, Bingfeng and Feng, Yansong and Xu, Jianbo and Zhang, Xiang and Zhao, Dongyan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2727--2736

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.

null

10.18653/v1/D17-1289

null

57,777

inproceedings

schofield-etal-2017-quantifying

Quantifying the Effects of Text Duplication on Semantic Models

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1290/

Schofield, Alexandra and Thompson, Laure and Mimno, David

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2737--2747

Duplicate documents are a pervasive problem in text datasets and can have a strong effect on unsupervised models. Methods to remove duplicate texts are typically heuristic or very expensive, so it is vital to know when and why they are needed. We measure the sensitivity of two latent semantic methods to the presence of different levels of document repetition. By artificially creating different forms of duplicate text we confirm several hypotheses about how repeated text impacts models. While a small amount of duplication is tolerable, substantial over-representation of subsets of the text may overwhelm meaningful topical patterns.

null

10.18653/v1/D17-1290

null

57,778

inproceedings

zhuang-etal-2017-identifying

Identifying Semantically Deviating Outlier Documents

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1291/

Zhuang, Honglei and Wang, Chi and Tao, Fangbo and Kaplan, Lance and Han, Jiawei

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2748--2757

A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus. Automatic identification of document outliers can be valuable in many applications, such as screening health records for medical mistakes. In this paper, we study the problem of mining semantically deviating document outliers in a given corpus. We develop a generative model to identify frequent and characteristic semantic regions in the word embedding space to represent the given corpus, and a robust outlierness measure which is resistant to noisy content in documents. Experiments conducted on two real-world textual data sets show that our method can achieve an up to 135{\%} improvement over baselines in terms of recall at top-1{\%} of the outlier ranking.

null

10.18653/v1/D17-1291

null

57,779

inproceedings

kang-etal-2017-detecting

Detecting and Explaining Causes From Text For a Time Series Event

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1292/

Kang, Dongyeop and Gangal, Varun and Lu, Ang and Chen, Zheng and Hovy, Eduard

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2758--2767

Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.

null

10.18653/v1/D17-1292

null

57,780

inproceedings

jiang-etal-2017-novel

A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of {MOOC}

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1293/

Jiang, Zhuoxuan and Feng, Shanshan and Cong, Gao and Miao, Chunyan and Li, Xiaoming

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2768--2773

Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs). With massive learners being offered MOOCs, there is a demand that the forum contents within MOOCs need to be classified in order to facilitate both learners and instructors. Therefore we investigate a significant application, which is to associate forum threads to subtitles of video clips. This task can be regarded as a document ranking problem, and the key is how to learn a distinguishable text representation from word sequences and learners' behavior sequences. In this paper, we propose a novel cascade model, which can capture both the latent semantics and latent similarity by modeling MOOC data. Experimental results on two real-world datasets demonstrate that our textual representation outperforms state-of-the-art unsupervised counterparts for the application.

null

10.18653/v1/D17-1293

null

57,781

inproceedings

mysore-sathyendra-etal-2017-identifying

Identifying the Provision of Choices in Privacy Policy Text

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1294/

Mysore Sathyendra, Kanthashree and Wilson, Shomir and Schaub, Florian and Zimmeck, Sebastian and Sadeh, Norman

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2774--2779

Websites' and mobile apps' privacy policies, written in natural language, tend to be long and difficult to understand. Information privacy revolves around the fundamental principle of Notice and choice, namely the idea that users should be able to make informed decisions about what information about them can be collected and how it can be used. Internet users want control over their privacy, but their choices are often hidden in long and convoluted privacy policy texts. Moreover, little (if any) prior work has been done to detect the provision of choices in text. We address this challenge of enabling user choice by automatically identifying and extracting pertinent choice language in privacy policies. In particular, we present a two-stage architecture of classification models to identify opt-out choices in privacy policy text, labelling common varieties of choices with a mean F1 score of 0.735. Our techniques enable the creation of systems to help Internet users to learn about their choices, thereby effectuating notice and choice and improving Internet privacy.

null

10.18653/v1/D17-1294

null

57,782

inproceedings

goyal-etal-2017-empirical

An Empirical Analysis of Edit Importance between Document Versions

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1295/

Goyal, Tanya and Kelkar, Sachin and Agarwal, Manas and Grover, Jeenu

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2780--2784

In this paper, we present a novel approach to infer significance of various textual edits to documents. An author may make several edits to a document; each edit varies in its impact to the content of the document. While some edits are surface changes and introduce negligible change, other edits may change the content/tone of the document significantly. In this paper, we perform an analysis on the human perceptions of edit importance while reviewing documents from one version to the next. We identify linguistic features that influence edit importance and model it in a regression based setting. We show that the predicted importance by our approach is highly correlated with the human perceived importance, established by a Mechanical Turk study.

null

10.18653/v1/D17-1295

null

57,783

inproceedings

wang-etal-2017-transition

Transition-Based Disfluency Detection using {LSTM}s

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1296/

Wang, Shaolei and Che, Wanxiang and Zhang, Yue and Zhang, Meishan and Liu, Ting

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2785--2794

In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information. Compared with sequence labeling methods, it can capture non-local chunk-level features; compared with joint parsing and disfluency detection methods, it is free for noise in syntax. Experiments show that our model achieves state-of-the-art f-score of 87.5{\%} on the commonly used English Switchboard test set, and a set of in-house annotated Chinese data.

null

10.18653/v1/D17-1296

null

57,784

inproceedings

yannakoudakis-etal-2017-neural

Neural Sequence-Labelling Models for Grammatical Error Correction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1297/

Yannakoudakis, Helen and Rei, Marek and Andersen, {\O}istein E. and Yuan, Zheng

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2795--2806

We propose an approach to N-best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.

null

10.18653/v1/D17-1297

null

57,785

inproceedings

schmaltz-etal-2017-adapting

Adapting Sequence Models for Sentence Correction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1298/

Schmaltz, Allen and Kim, Yoon and Rush, Alexander and Shieber, Stuart

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2807--2813

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.

null

10.18653/v1/D17-1298

null

57,786

inproceedings

niu-etal-2017-study

A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1299/

Niu, Xing and Martindale, Marianna and Carpuat, Marine

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2814--2819

Stylistic variations of language, such as formality, carry speakers' intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.

null

10.18653/v1/D17-1299

null

57,787

inproceedings

devlin-2017-sharp

Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the {CPU}

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1300/

Devlin, Jacob

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2820--2825

Attentional sequence-to-sequence models have become the new standard for machine translation, but one challenge of such models is a significant increase in training and decoding cost compared to phrase-based systems. In this work we focus on efficient decoding, with a goal of achieving accuracy close the state-of-the-art in neural machine translation (NMT), while achieving CPU decoding speed/throughput close to that of a phrasal decoder. We approach this problem from two angles: First, we describe several techniques for speeding up an NMT beam search decoder, which obtain a 4.4x speedup over a very efficient baseline decoder without changing the decoder output. Second, we propose a simple but powerful network architecture which uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked fully-connected layers applied at every timestep. This architecture achieves similar accuracy to a deep recurrent model, at a small fraction of the training and decoding cost. By combining these techniques, our best system achieves a very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014, while decoding at 100 words/sec on single-threaded CPU. We believe this is the best published accuracy/speed trade-off of an NMT system.

null

10.18653/v1/D17-1300

null

57,788

inproceedings

wang-etal-2017-exploiting-cross

Exploiting Cross-Sentence Context for Neural Machine Translation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1301/

Wang, Longyue and Tu, Zhaopeng and Way, Andy and Liu, Qun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2826--2831

In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.

null

10.18653/v1/D17-1301

null

57,789

inproceedings

kim-etal-2017-cross

Cross-Lingual Transfer Learning for {POS} Tagging without Cross-Lingual Resources

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1302/

Kim, Joo-Kyung and Kim, Young-Bum and Sarikaya, Ruhi and Fosler-Lussier, Eric

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2832--2838

Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.

null

10.18653/v1/D17-1302

null

57,790

inproceedings

gella-etal-2017-image

Image Pivoting for Learning Multilingual Multimodal Representations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1303/

Gella, Spandana and Sennrich, Rico and Keller, Frank and Lapata, Mirella

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2839--2845

In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.

null

10.18653/v1/D17-1303

null

57,791

inproceedings

chen-etal-2017-neural

Neural Machine Translation with Source Dependency Representation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1304/

Chen, Kehai and Wang, Rui and Utiyama, Masao and Liu, Lemao and Tamura, Akihiro and Sumita, Eiichiro and Zhao, Tiejun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2846--2852

Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.

null

10.18653/v1/D17-1304

null

57,792

inproceedings

han-etal-2017-visual

Visual Denotations for Recognizing Textual Entailment

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1305/

Han, Dan and Mart{\'i}nez-G{\'o}mez, Pascual and Mineshima, Koji

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2853--2859

In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their meaning in terms of their images. We show that our approach is effective in the task of Recognizing Textual Entailment when combined with specific linguistic and logic features.

null

10.18653/v1/D17-1305

null

57,793

inproceedings

mathur-etal-2017-sequence

Sequence Effects in Crowdsourced Annotations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1306/

Mathur, Nitika and Baldwin, Timothy and Cohn, Trevor

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2860--2865

Manual data annotation is a vital component of NLP research. When designing annotation tasks, properties of the annotation interface can unintentionally lead to artefacts in the resulting dataset, biasing the evaluation. In this paper, we explore sequence effects where annotations of an item are affected by the preceding items. Having assigned one label to an instance, the annotator may be less (or more) likely to assign the same label to the next. During rating tasks, seeing a low quality item may affect the score given to the next item either positively or negatively. We see clear evidence of both types of effects using auto-correlation studies over three different crowdsourced datasets. We then recommend a simple way to minimise sequence effects.

null

10.18653/v1/D17-1306

null

57,794

inproceedings

ture-jojic-2017-need

No Need to Pay Attention: Simple Recurrent Neural Networks Work!

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1307/

Ture, Ferhan and Jojic, Oliver

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2866--2872

First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65{\%}{--}76{\%} accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results {---} even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast`s X1 entertainment platform with millions of users every day.

null

10.18653/v1/D17-1307

null

57,795

inproceedings

mimno-thompson-2017-strange

The strange geometry of skip-gram with negative sampling

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1308/

Mimno, David and Thompson, Laure

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2873--2878

Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms.

null

10.18653/v1/D17-1308

null

57,796

inproceedings

botha-etal-2017-natural

Natural Language Processing with Small Feed-Forward Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1309/

Botha, Jan A. and Pitler, Emily and Ma, Ji and Bakalov, Anton and Salcianu, Alex and Weiss, David and McDonald, Ryan and Petrov, Slav

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2879--2885

We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.

null

10.18653/v1/D17-1309

null

57,797

inproceedings

li-lam-2017-deep

Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1310/

Li, Xin and Lam, Wai

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2886--2892

We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.

null

10.18653/v1/D17-1310

null

57,798

inproceedings

andreas-klein-2017-analogs

Analogs of Linguistic Structure in Deep Representations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1311/

Andreas, Jacob and Klein, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2893--2897

We investigate the compositional structure of message vectors computed by a deep network trained on a communication game. By comparing truth-conditional representations of encoder-produced message vectors to human-produced referring expressions, we are able to identify aligned (vector, utterance) pairs with the same meaning. We then search for structured relationships among these aligned pairs to discover simple vector space transformations corresponding to negation, conjunction, and disjunction. Our results suggest that neural representations are capable of spontaneously developing a {\textquotedblleft}syntax{\textquotedblright} with functional analogues to qualitative properties of natural language.

null

10.18653/v1/D17-1311

null

57,799

inproceedings

yang-etal-2017-simple

A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1312/

Yang, Wei and Lu, Wei and Zheng, Vincent

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2898--2904

Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is less explored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.

null

10.18653/v1/D17-1312

null

57,800

inproceedings

noriega-atala-etal-2017-learning

Learning what to read: Focused machine reading

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1313/

Noriega-Atala, Enrique and Valenzuela-Esc{\'a}rcega, Marco A. and Morrison, Clayton and Surdeanu, Mihai

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

2905--2910

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today`s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.

null

10.18653/v1/D17-1313

null

57,801