mtybilly/Instruct2DS huggingface dataset:
Learning Joint Semantic Parsers from Disjoint Data
Authors: Sam Thomson, Swabha Swayamdipta, Noah A. Smith Hao Peng
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: We present a new approach to learning a semantic parser from multiple datasets, even when the target semantic formalisms are drastically different and the underlying corpora do not overlap. We handle such “disjoint” data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.
Identifying Semantic Divergences in Parallel Text without Annotations
Authors: Xing Niu, Marine Carpuat Yogarshi Vyas
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.
Bootstrapping Generators from Noisy Data
Authors: Mirella Lapata Laura Perez-Beltrachini
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation
Authors: Nan Ding, Radu Soricut Ye Zhang
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an \textquoteleftaverage' style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the-fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
Authors: Shreyas Shetty, Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Mitesh M. Khapra Preksha Nema
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, actor, director, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit task specific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21% relative improvement over a recently proposed state of the art method and 10% relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available on r̆lhttps://github.com/PrekshaNema25/StructuredData_To_Descriptions
CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension
Authors: Walter Daelemans Simon Šuster
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20% F1) between the best human and machine readers. We analyze the skills required for successful answering and show how reader performance varies depending on the applicable skills. We find that inferences using domain knowledge and object tracking are the most frequently required skills, and that recognizing omitted information and spatio-temporal reasoning are the most difficult for the machines.
Learning to Collaborate for Question Answering and Asking
Authors: Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou Duyu Tang
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In this paper, we give a systematic study that seeks to leverage the connection to improve both QA and QG. We present a training algorithm that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario. The two key ideas are improving the QG model with QA through incorporating additional QA-specific signal as the loss function, and improving the QA model with QG through adding artificially generated training instances. We conduct experiments on both document based and knowledge based question answering tasks. We have two main findings. Firstly, the performance of a QG model (e.g in terms of BLEU score) could be easily improved by a QA model via policy gradient. Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Learning when to regard generated questions as positive instances could bring performance boost.
Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering
Authors: Joongbo Shin, Kyomin Jung Seunghyun Yoon
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.
Supervised and Unsupervised Transfer Learning for Question Answering
Authors: Hung-Yi Lee, James Glass Yu-An Chung
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.
Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension
Authors: Lifu Huang, Niket Tandon, Wen-tau Yih, Peter Clark Bhavana Dalvi
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints). The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred. We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction. The new models improve accuracy by up to 19%. We are releasing the ProPara dataset and our models to the community.
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation
Authors: Xin Luo, Huiling Zhu, Hankz Hankui Zhuo Mingmin Jin
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant information within user reviews is very useful for extracting user preferences and item properties. In this paper, we investigate the approach to effectively utilize review information for recommender systems. The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding. In the experiments on popular review dataset Amazon , our LTMF model outperforms previous proposed HFT model and ConvMF model in rating prediction. Furthermore, LTMF shows the better ability on making topic clustering than traditional topic model based method, which implies integrating the information from deep learning and topic modeling is a meaningful approach to make a better understanding of reviews.
Deconfounded Lexicon Induction for Interpretable Social Science
Authors: Kelly Shen, Dan Jurafsky, Stefan Wagner Reid Pryzant
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: NLP algorithms are increasingly used in computational social science to take linguistic observations and predict outcomes like human preferences or actions. Making these social models transparent and interpretable often requires identifying features in the input that predict outcomes while also controlling for potential confounds. We formalize this need as a new task: inducing a lexicon that is predictive of a set of target variables yet uncorrelated to a set of confounding variables. We introduce two deep learning algorithms for the task. The first uses a bifurcated architecture to separate the explanatory power of the text and confounds. The second uses an adversarial discriminator to force confound-invariant text encodings. Both elicit lexicons from learned weights and attentional scores. We use them to induce lexicons that are predictive of timely responses to consumer complaints (controlling for product), enrollment from course descriptions (controlling for subject), and sales from product descriptions (controlling for seller). In each domain our algorithms pick words that are associated with \textitnarrative persuasion; more predictive and less confound-related than those of standard feature weighting and lexicon induction techniques like regression and log odds.
Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security
Authors: Ben Fry, James McMasters Nathanael Chambers
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: This paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so a detection system from public data could better assist a response to a broad attack across multiple services. We explore NLP methods to use social media as an indirect measure of network service status. We describe two learning frameworks for this task: a feed-forward neural network and a partially labeled LDA model. Both models outperform previous work by significant margins (20% F1 score). We further show that the topic-based model enables the first fine-grained analysis of how the public reacts to ongoing network attacks, discovering multiple “stages” of observation. This is the first model that both detects network attacks (with best performance) and provides an analysis of when and how the public interprets service outages. We describe the models, present experiments on the largest twitter DDoS corpus to date, and conclude with an analysis of public reactions based on the learned model`s output.
The Importance of Calibration for Estimating Proportions from Annotations
Authors: Noah A. Smith Dallas Card
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Estimating label proportions in a target corpus is a type of measurement that is useful for answering certain types of social-scientific questions. While past work has described a number of relevant approaches, nearly all are based on an assumption which we argue is invalid for many problems, particularly when dealing with human annotations. In this paper, we identify and differentiate between two relevant data generating scenarios (intrinsic vs. extrinsic labels), introduce a simple but novel method which emphasizes the importance of calibration, and then analyze and experimentally validate the appropriateness of various methods for each of the two scenarios.
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Authors: Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz Dongyeop Kang
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1),1 providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as \textquoteleftoriginality' and \textquoteleftimpact'.
Deep Communicating Agents for Abstractive Summarization
Authors: Antoine Bosselut, Xiaodong He, Yejin Choi Asli Celikyilmaz
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts
Authors: Jing Li, Yan Song, Chengzhi Zhang Yingyi Zhang
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.
Estimating Summary Quality with Pairwise Preferences
Authors: Markus Zopf
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Automatic evaluation systems in the field of automatic summarization have been relying on the availability of gold standard summaries for over ten years. Gold standard summaries are expensive to obtain and often require the availability of domain experts to achieve high quality. In this paper, we propose an alternative evaluation approach based on pairwise preferences of sentences. In comparison to gold standard summaries, they are simpler and cheaper to obtain. In our experiments, we show that humans are able to provide useful feedback in the form of pairwise preferences. The new framework performs better than the three most popular versions of ROUGE with less expensive human input. We also show that our framework can reuse already available evaluation data and achieve even better results.
Generating Topic-Oriented Summaries Using Neural Attention
Authors: Balaji Vasan Srinivasan Kundan Krishna
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do justice to all the topics. Further, the interests of readers can vary and the notion of importance can change across them. Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. In this paper, we propose an attention based RNN framework to generate multiple summaries of a single document tuned to different topics of interest. Our method outperforms existing baselines and our results suggest that the attention of generative networks can be successfully biased to look at sentences relevant to a topic and effectively used to generate topic-tuned summaries.
Generative Bridging Network for Neural Sequence Prediction
Authors: Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou Wenhu Chen
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE directly maximizing the conditional likelihood, the bridge extends the point-wise ground truth to a bridge distribution conditioned on it, and the generator is optimized to minimize their KL-divergence. Three different GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to penalize confidence, enhance language smoothness and relieve learning burden. Experiments conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) show that our proposed GBNs can yield significant improvements over strong baselines. Furthermore, by analyzing samples drawn from different bridges, expected influences on the generator are verified.
Higher-Order Syntactic Attention Network for Longer Sentence Compression
Authors: Katsuhiko Hayashi, Tsutomu Hirao, Masaaki Nagata Hidetaka Kamigaito
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: A sentence compression method using LSTM can generate fluent compressed sentences. However, the performance of this method is significantly degraded when compressing longer sentences since it does not explicitly handle syntactic features. To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states. Furthermore, to avoid the influence of incorrect parse results, we trained HiSAN by maximizing jointly the probability of a correct output with the attention distribution. Experimental results on Google sentence compression dataset showed that our method achieved the best performance on F1 as well as ROUGE-1,2 and L scores, 83.2, 82.9, 75.8 and 82.7, respectively. In human evaluation, our methods also outperformed baseline methods in both readability and informativeness.
Neural Storyline Extraction Model for Storyline Generation from News Articles
Authors: Linsen Guo, Yulan He Deyu Zhou
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Storyline generation aims to extract events described on news articles under a certain news topic and reveal how those events evolve over time. Most approaches to storyline generation first train supervised models to extract events from news articles published in different time periods and then link relevant extracted events into coherent stories. They are domain dependent and cannot deal with unseen event types. To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without the use of annotated data. However, the parameter inference procedure is too complex and models often require long time to converge. In this paper, we propose a novel neural network based approach to extract structured representations and evolution patterns of storylines without using annotated data. In this model, title and main body of a news article are assumed to share the similar storyline distribution. Moreover, similar documents described in neighboring time periods are assumed to share similar storyline distributions. Based on these assumptions, structured representations and evolution patterns of storylines can be extracted. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms state-of-the-art approaches for storyline generation on both accuracy and efficiency.
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function
Authors: Tsutomu Hirao, Masaaki Nishino, Masaaki Nagata Shinsaku Sakaue
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Submodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular objective functions, the efficiency of the greedy algorithm, and the provable performance guarantee. However, when it comes to compressive summarization, we are currently missing a counterpart of the extractive method based on submodularity. In this paper, we propose a fast greedy method for compressive summarization. Our method is applicable to any monotone submodular objective function, including many functions well-suited for document summarization. We provide an approximation guarantee of our greedy algorithm. Experiments show that our method is about 100 to 400 times faster than an existing method based on integer-linear-programming (ILP) formulations and that our method empirically achieves more than 95%-approximation.
Ranking Sentences for Extractive Summarization with Reinforcement Learning
Authors: Shay B. Cohen, Mirella Lapata Shashi Narayan
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
Relational Summarization for Corpus Analysis
Authors: Brendan O′Connor Abram Handler
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: This work introduces a new problem, relational summarization, in which the goal is to generate a natural language summary of the relationship between two lexical items in a corpus, without reference to a knowledge base. Motivated by the needs of novel user interfaces, we define the task and give examples of its application. We also present a new query-focused method for finding natural language sentences which express relationships. Our method allows for summarization of more than two times more query pairs than baseline relation extractors, while returning measurably more readable output. Finally, to help guide future work, we analyze the challenges of relational summarization using both a news and a social media corpus.
What`s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis
Authors: Mirella Lapata Philip John Gorinski
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impression of a movie, describing aspects such as its genre, plot, mood, or artistic style. We create a dataset that consists of movie scripts, attribute-value pairs for the movies' aspects, as well as overviews, which we extract from an online database. We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. Automatic and human evaluation show that the encoder is able to reliably assign good labels for the movie`s attributes, and the overviews provide descriptions of the movie`s content which are informative and faithful.
Which Scores to Predict in Sentence Regression for Text Summarization?
Authors: Eneldo Loza Mencía, Johannes Fürnkranz Markus Zopf
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: The task of automatic text summarization is to generate a short text that summarizes the most important information in a given set of documents. Sentence regression is an emerging branch in automatic text summarizations. Its key idea is to estimate the importance of information via learned utility scores for individual sentences. These scores are then used for selecting sentences from the source documents, typically according to a greedy selection strategy. Recently proposed state-of-the-art models learn to predict ROUGE recall scores of individual sentences, which seems reasonable since the final summaries are evaluated according to ROUGE recall. In this paper, we show in extensive experiments that following this intuition leads to suboptimal results and that learning to predict ROUGE precision scores leads to better results. The crucial difference is to aim not at covering as much information as possible but at wasting as little space as possible in every greedy step.
A Hierarchical Latent Structure for Variational Conversation Modeling
Authors: Jaemin Cho, Gunhee Kim Yookoon Park
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.
Detecting Egregious Conversations between Customers and Virtual Agents
Authors: Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski Tommy Sandbank
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20% over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking
Authors: Francine Chen, Yan-Ying Chen, Wei Wang Jyun-Yu Jiang
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese hierarchical convolutional neural network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for conversation identification by similarity ranking (CISIR) then derives conversations based on high-confidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.
Variational Knowledge Graph Reasoning
Authors: Wenhan Xiong, Xifeng Yan, William Yang Wang Wenhu Chen
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (Diva) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, Diva is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.
Inducing Temporal Relations from Time Anchor Annotation
Authors: Yusuke Miyao Fei Cheng
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Recognizing temporal relations among events and time expressions has been an essential but challenging task in natural language processing. Conventional annotation of judging temporal relations puts a heavy load on annotators. In reality, the existing annotated corpora include annotations on only “salient” event pairs, or on pairs in a fixed window of sentences. In this paper, we propose a new approach to obtain temporal relations from absolute time value (a.k.a. time anchors), which is suitable for texts containing rich temporal information such as news articles. We start from time anchors for events and time expressions, and temporal relation annotations are induced automatically by computing relative order of two time anchors. This proposal shows several advantages over the current methods for temporal relation annotation: it requires less annotation effort, can induce inter-sentence relations easily, and increases informativeness of temporal relations. We compare the empirical statistics and automatic recognition results with our data against a previous temporal relation corpus. We also reveal that our data contributes to a significant improvement of the downstream time anchor prediction task, demonstrating 14.1 point increase in overall accuracy.
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
Authors: Partha Talukdar, Vasudeva Varma Priya Radhakrishnan
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system`s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms state-of-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN`s approach is simple, yet effective. We have made ELDEN`s code and data publicly available.
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
Authors: Xin Jiang, Zhunchen Luo, Wenhan Chao Hai Ye
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer
Authors: Robin Jia, He He, Percy Liang Juncen Li
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.g., sentiment) while preserving its attribute-independent content (e.g., “screen is just the right size” to “screen is too small”). Our training data includes only sentences labeled with their attribute (e.g., positive and negative), but not pairs of sentences that only differ in the attributes, so we must learn to disentangle attributes from attribute-independent content in an unsupervised way. Previous work using adversarial methods has struggled to produce high-quality outputs. In this paper, we propose simpler methods motivated by the observation that text attributes are often marked by distinctive phrases (e.g., “too small”). Our strongest method extracts content words by deleting phrases associated with the sentence`s original attribute value, retrieves new phrases associated with the target attribute, and uses a neural model to fluently combine these into a final output. Based on human evaluation, our best method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets: altering sentiment of reviews on Yelp, altering sentiment of reviews on Amazon, and altering image captions to be more romantic or humorous.
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Authors: John Wieting, Kevin Gimpel, Luke Zettlemoyer Mohit Iyyer
Published in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Main Conference Track), 2018

Abstract: We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) “fool” pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.