mtybilly/Instruct2DS huggingface dataset:
A Dataset for Document Grounded Conversations
Authors: Kangyan Zhou, Shrimai Prabhumoye, Alan W Black
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper introduces a document grounded dataset for conversations. We define “Document Grounded Conversations” as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.
Out-of-domain Detection based on Generative Adversarial Network
Authors: Seonghan Ryu, Sangjun Koo, Hwanjo Yu, Gary Geunbae Lee
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems. We propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. To improve basic GANs, we apply feature matching loss in the discriminator, use domain-category analysis as an additional task in the discriminator, and remove the biases in the generator. Thereby, we reduce the huge effort of collecting OOD sentences for training OOD detection. For evaluation, we experimented OOD detection on a multi-domain dialog system. The experimental results showed the proposed method was most accurate compared to the existing methods.
Listening Comprehension over Argumentative Content
Authors: Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.
Using active learning to expand training data for implicit discourse relation recognition
Authors: Yang Xu, Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, Guodong Zhou
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely employed to boost semantic relation discrimination. However, learning to predict semantic relations at a deep level heavily relies on a great deal of training data, but the scale of the publicly available data in this field is limited. In this paper, we follow Rutherford and Xue (2015) to expand the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. On the basis, we carry out an experiment of sampling, in which a simple active learning approach is used, so as to take the informative instances for data expansion. The goal is to verify whether the selective use of external data not only reduces the time consumption of retraining but also ensures a better system performance. Using the expanded training data, we retrain a convolutional neural network (CNN) based classifer which is a simplified version of Qin et al. (2016)`s stacking gated relation recognizer. Experimental results show that expanding the training set with small-scale carefully-selected external data yields substantial performance gain, with the improvements of about 4% for accuracy and 3.6% for F-score. This allows a weak classifier to achieve a comparable performance against the state-of-the-art systems.
Learning To Split and Rephrase From Wikipedia Edit History
Authors: Jan A. Botha, Manaal Faruqui, John Alex, Jason Baldridge, Dipanjan Das
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia`s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.
BLEU is Not Suitable for the Evaluation of Text Simplification
Authors: Elior Sulem, Omri Abend, Ari Rappoport
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information
Authors: Jiaxin Pei, Chenliang Li
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: How to generate relevant and informative responses is one of the core topics in response generation area. Following the task formulation of machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. To realize this mapping, existing works tend to design intuitive but complex models. However, the relevant information existed in large dialogue corpus is mainly overlooked. In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation. Specifically, we devise two simple approaches in S2SPMN to select the relevant information (named prototypes) from the dialogue corpus. These prototypes are then saved into prototype memory network (PMN). Furthermore, a hierarchical attention mechanism is devised to extract the semantic information from the PMN to assist the response generation process. Empirical studies reveal the advantage of our model over several classical and strong baselines.
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior
Authors: Tszhang Guo, Shiyu Chang, Mo Yu, Kun Bai
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unstable and slow. To alleviate these issues, we propose a simple coherent solution that constrains the action space using an n-gram language prior. Quantitative and qualitative evaluations on benchmarks show that RL with the simple add-on module performs favorably against its counterpart in terms of both readability and speed of convergence. Human evaluation results show that our model is more human readable and graceful. The implementation will become publicly available upon the acceptance of the paper.
Training for Diversity in Image Paragraph Captioning
Authors: Luke Melas-Kyriazi, Alexander Rush, George Han
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Image paragraph captioning models aim to produce detailed descriptions of a source image. These models use similar techniques as standard image captioning models, but they have encountered issues in text generation, notably a lack of diversity between sentences, that have limited their effectiveness. In this work, we consider applying sequence-level training for this task. We find that standard self-critical training produces poor results, but when combined with an integrated penalty on trigram repetition produces much more diverse paragraphs. This simple training approach improves on the best result on the Visual Genome paragraph captioning dataset from 16.9 to 30.6 CIDEr, with gains on METEOR and BLEU as well, without requiring any architectural changes.
A Graph-theoretic Summary Evaluation for ROUGE
Authors: Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: ROUGE is one of the first and most widely used evaluation metrics for text summarization. However, its assessment merely relies on surface similarities between peer and model summaries. Consequently, ROUGE is unable to fairly evaluate summaries including lexical variations and paraphrasing. We propose a graph-based approach adopted into ROUGE to evaluate summaries based on both lexical and semantic similarities. Experiment results over TAC AESOP datasets show that exploiting the lexico-semantic similarity of the words used in summaries would significantly help ROUGE correlate better with human judgments.
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation
Authors: Hardy Hardy, Andreas Vlachos
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document. We demonstrate that this guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. We also find that the summarization performance on later parses is 2 ROUGE-2 points higher than that of a well-established neural encoder-decoder approach trained on a larger dataset.
Evaluating Multiple System Summary Lengths: A Case Study
Authors: Ori Shapira, David Gabay, Hadar Ronen, Judit Bar-Ilan, Yael Amsterdamer, Ani Nenkova, Ido Dagan
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.
Neural Latent Extractive Document Summarization
Authors: Xingxing Zhang, Mirella Lapata, Furu Wei, Ming Zhou
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.
On the Abstractiveness of Neural Document Summarization
Authors: Fangfang Zhang, Jin-ge Yao, Rui Yan
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries. We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units. Upon the observation that many abstractive systems tend to be near-extractive in practice, we also implemented a pure copy system, which achieved comparable results as abstractive summarizers while being far more computationally efficient. These findings suggest the possibility for future efforts towards more efficient systems that could better utilize the vocabulary in the original document.
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning
Authors: Yucheng Wang, Zhongyu Wei, Yaqian Zhou, Xuanjing Huang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.
Identifying Well-formed Natural Language Questions
Authors: Manaal Faruqui, Dipanjan Das
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.
Self-Governing Neural Networks for On-Device Short Text Classification
Authors: Sujith Ravi, Zornitsa Kozareva
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
Authors: Kazuya Shimura, Jiyi Li, Fumiyo Fukumoto
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. The lower the HS level, the less the categorization performance. Because the number of training data per category in a lower level is much smaller than that in an upper level. We propose an approach which can effectively utilize the data in the upper levels to contribute the categorization in the lower levels by applying the Convolutional Neural Network (CNN) with a fine-tuning technique. The results using two benchmark datasets show that proposed method, Hierarchical Fine-Tuning based CNN (HFT-CNN) is competitive with the state-of-the-art CNN based methods.
A Hierarchical Neural Attention-based Text Classifier
Authors: Koustuv Sinha, Yue Dong, Jackie Chi Kit Cheung, Derek Ruths
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
Authors: Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, Kevin Seppi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.
Coherence-Aware Neural Topic Modeling
Authors: Ran Ding, Ramesh Nallapati, Bing Xiang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models
Authors: Daniel Watson, Nasser Zalmout, Nizar Habash
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the complexity of the language increase the difficulty of the problem. To address these challenges, we use a sequence-to-sequence model with character-based attention, which in addition to its self-learned character embeddings, uses word embeddings pre-trained with an approach that also models subword information. This provides the neural model with access to more linguistic information especially suitable for text normalization, without large parallel corpora. We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.
Topic Intrusion for Automatic Topic Model Evaluation
Authors: Shraey Bhatia, Jey Han Lau, Timothy Baldwin
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task --- the task of guessing an outlier topic given a document and a few topics --- and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents
Authors: Abhijith Athreya Mysore Gopinath, Shomir Wilson, Norman Sadeh
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The text in many web documents is organized into a hierarchy of section titles and corresponding prose content, a structure which provides potentially exploitable information on discourse structure and topicality. However, this organization is generally discarded during text collection, and collecting it is not straightforward: the same visual organization can be implemented in a myriad of different ways in the underlying HTML. To remedy this, we present a flexible system for automatically extracting the hierarchical section titles and prose organization of web documents irrespective of differences in HTML representation. This system uses features from syntax, semantics, discourse and markup to build two models which classify HTML text into section titles and prose text. When tested on three different domains of web text, our domain-independent system achieves an overall precision of 0.82 and a recall of 0.98. The domain-dependent variation produces very high precision (0.99) at the expense of recall (0.75). These results exhibit a robust level of accuracy suitable for enhancing question answering, information extraction, and summarization.
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Authors: Xinyi Wang, Hieu Pham, Zihang Dai, Graham Neubig
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
Authors: Yunsu Kim, Jiahui Geng, Hermann Ney
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.
Decipherment of Substitution Ciphers with Neural Language Models
Authors: Nishant Kambhatla, Anahita Mansouri Bigvand, Anoop Sarkar
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.
Rapid Adaptation of Neural Machine Translation to New Languages
Authors: Graham Neubig, Junjie Hu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual “seed models”, which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of “similar-language regularization”, where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.
Compact Personalized Models for Neural Machine Translation
Authors: Joern Wuebker, Patrick Simianer, John DeNero
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture--combining a state-of-the-art self-attentive model with compact domain adaptation--provides high quality personalized machine translation that is both space and time efficient.
Supervised Domain Enablement Attention for Personalized Domain Classification
Authors: Joo-Kyung Kim, Young-Bum Kim
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In large-scale domain classification for natural language understanding, leveraging each user`s domain enablement information, which refers to the preferred or authenticated domains by the user, with attention mechanism has been shown to improve the overall domain classification performance. In this paper, we propose a supervised enablement attention mechanism, which utilizes sigmoid activation for the attention weighting so that the attention can be computed with more expressive power without the weight sum constraint of softmax attention. The attention weights are explicitly encouraged to be similar to the corresponding elements of the output one-hot vector, and self-distillation is used to leverage the attention information of the other enabled domains. By evaluating on the actual utterances from a large-scale IPDA, we show that our approach significantly improves domain classification performance
A Deep Neural Network Sentence Level Classification Method with Context Information
Authors: Xingyi Song, Johann Petrak, Angus Roberts
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.
Towards Dynamic Computation Graphs via Sparse Latent Structure
Authors: Vlad Niculae, André F. T. Martins, Claire Cardie
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Using the recently proposed SparseMAP inference, which retrieves a sparse distribution over latent structures, we propose a novel approach for end-to-end learning of latent structure predictors jointly with a downstream predictor. To the best of our knowledge, our method is the first to enable unrestricted dynamic computation graph construction from the global latent structure, while maintaining differentiability.
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Authors: Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, Vadim Sheinin
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features; then employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.
Retrieval-Based Neural Code Generation
Authors: Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.
SQL-to-Text Generation with Graph-to-Sequence Model
Authors: Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we propose a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.
Generating Syntactic Paraphrases
Authors: Emilie Colin, Claire Gardent
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.
Neural-Davidsonian Semantic Proto-role Labeling
Authors: Rachel Rudinger, Adam Teichert, Ryan Culkin, Sheng Zhang, Benjamin Van Durme
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.
Conversational Decision-Making Model for Predicting the King`s Decision in the Annals of the Joseon Dynasty
Authors: JinYeong Bak, Alice Oh
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Styles of leaders when they make decisions in groups vary, and the different styles affect the performance of the group. To understand the key words and speakers associated with decisions, we initially formalize the problem as one of predicting leaders' decisions from discussion with group members. As a dataset, we introduce conversational meeting records from a historical corpus, and develop a hierarchical RNN structure with attention and pre-trained speaker embedding in the form of a, Conversational Decision Making Model (CDMM). The CDMM outperforms other baselines to predict leaders' final decisions from the data. We explain why CDMM works better than other methods by showing the key words and speakers discovered from the attentions as evidence.
Toward Fast and Accurate Neural Discourse Segmentation
Authors: Yizhong Wang, Sujian Li, Jingfeng Yang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.
A Dataset for Telling the Stories of Social Media Videos
Authors: Spandana Gella, Mike Lewis, Marcus Rohrbach
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.
Cascaded Mutual Modulation for Visual Reasoning
Authors: Yiqun Yao, Jiaming Xu, Feng Wang, Bo Xu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM: Cascaded Mutual Modulation as a novel end-to-end visual reasoning model. CMM includes a multi-step comprehension process for both question and image. In each step, we use a Feature-wise Linear Modulation (FiLM) technique to enable textual/visual pipeline to mutually control each other. Experiments show that CMM significantly outperforms most related models, and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR, collected from both synthetic and natural languages. Ablation studies confirm the effectiveness of CMM to comprehend natural language logics under the guidence of images. Our code is available at r̆lhttps://github.com/FlamingHorizon/CMM-VR.
How agents see things: On visual representations in an emergent language game
Authors: Diane Bouchacourt, Marco Baroni
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents' symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction. We find that the agents establish successful communication by inducing visual representations that almost perfectly align with each other, but, surprisingly, do not capture the conceptual properties of the objects depicted in the input images. We conclude that, if we care about developing language-like communication systems, we must pay more attention to the visual semantics agents associate to the symbols they use.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
Authors: Ningyu Zhang, Shumin Deng, Zhanling Sun, Xi Chen, Wei Zhang, Huajun Chen
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.
Put It Back: Entity Typing with Language Model Enhancement
Authors: Ji Xin, Hao Zhu, Xu Han, Zhiyuan Liu, Maosong Sun
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from r̆lhttps://github.com/thunlp/LME.
Event Detection with Neural Networks: A Rigorous Empirical Evaluation
Authors: Walker Orr, Prasad Tadepalli, Xiaoli Fern
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset.
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages
Authors: Yiqing Zhang, Jianzhong Qi, Rui Zhang, Chuandong Yin
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Publication information in a researcher`s academic homepage provides insights about the researcher`s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8% in F1-score.