pwc-archive/papers-with-abstracts huggingface dataset:
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Authors: Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Informativeness, Reranking, Sentence Summarization, Summarization

Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Authors: Saif Mohammad
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Emotion Recognition

Words play a central role in language and thought. Factor analysis studies have shown that the primary dimensions of meaning are valence, arousal, and dominance (VAD). We present the NRC VAD Lexicon, which has human ratings of valence, arousal, and dominance for more than 20,000 English words. We use Best{--}Worst Scaling to obtain fine-grained scores and address issues of annotation consistency that plague traditional rating scale methods of annotation. We show that the ratings obtained are vastly more reliable than those in existing lexicons. We also show that there exist statistically significant differences in the shared understanding of valence, arousal, and dominance across demographic variables such as age, gender, and personality.
Retrieval of the Best Counterargument without Prior Topic Knowledge
Authors: Henning Wachsmuth, Shahbaz Syed, Benno Stein
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Retrieval

Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments{'} premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60{\%} accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.
Bridging CNNs, RNNs, and Weighted Finite-State Machines
Authors: Roy Schwartz, Sam Thomson, Noah A. Smith
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, Representation Learning, Small Data Image Classification, text-classification, Text Classification

Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with weighted finite-state automata (WFSAs) to learn a soft version of traditional surface patterns. We show that SoPa is an extension of a one-layer CNN, and that such CNNs are equivalent to a restricted version of SoPa, and accordingly, to a restricted form of WFSA. Empirically, on three text classification tasks, SoPa is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline, and is particularly useful in small data settings.
Zero-shot Learning of Classifiers from Natural Language Quantification
Authors: Shashank Srivastava, Igor Labutov, Tom Mitchell
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Parsing, Zero-Shot Learning

Humans can efficiently learn new concepts using language. We present a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed attributes of unlabeled data, and leverage the differential semantics of linguistic quantifiers (e.g., {`}usually{'} vs {`}always{'}) to drive model training. Experiments on three domains show that the learned classifiers outperform previous approaches for learning with limited data, and are comparable with fully supervised classifiers trained from a small number of labeled examples.
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Authors: Nina Poerner, Hinrich Sch{\"u}tze, Benjamin Roth
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentiment Analysis

The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.
Accurate SHRG-Based Semantic Parsing
Authors: Yufei Chen, Weiwei Sun, Xiaojun Wan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Composition, Semantic Parsing

We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process. Our parser achieves an accuracy of 90.35 for EDS (89.51 for DMRS) in terms of elementary dependency match, which is a 4.87 (5.45) point improvement over the best existing data-driven model, indicating, in our view, the importance of linguistically-informed derivation for data-driven semantic parsing. This accuracy is equivalent to that of English Resource Grammar guided models, suggesting that (recurrent) neural network models are able to effectively learn deep linguistic knowledge from annotations.
Using Intermediate Representations to Solve Math Word Problems
Authors: Danqing Huang, Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Math, Math Word Problem Solving

To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations.
Discourse Representation Structure Parsing
Authors: Jiangming Liu, Shay B. Cohen, Mirella Lapata
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Prediction, Question Answering, Semantic Parsing

We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages: basic DRS structure prediction, condition prediction (i.e., predicates and relations), and referent prediction (i.e., variables). Experimental results on the Groningen Meaning Bank (GMB) show that our model outperforms competitive baselines by a wide margin.
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
Authors: Prafulla Kumar Choubey, Ruihong Huang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution, Question Answering

This paper proposes a novel approach for event coreference resolution that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. We explicitly model correlations between the main event chains of a document with topic transition sentences, inter-coreference chain correlations, event mention distributional characteristics and sub-event structure, and use them with scores obtained from a local coreference relation classifier for jointly resolving multiple event chains in a document. Our experiments across KBP 2016 and 2017 datasets suggest that each of the structures contribute to improving event coreference resolution performance.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Authors: Xiangrong Zeng, Daojian Zeng, Shizhu He, Kang Liu, Jun Zhao
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Feature Engineering, Relation Extraction, Sentence, Triplet

The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection
Authors: Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Event Detection, Feature Engineering, Generative Adversarial Network

Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable.
Context-Aware Neural Model for Temporal Information Extraction
Authors: Yuanliang Meng, Anna Rumshisky
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Information Retrieval, model, Question Answering, Temporal Information Extraction

We propose a context-aware neural network model for temporal information extraction. This model has a uniform architecture for event-event, event-timex and timex-timex pairs. A Global Context Layer (GCL), inspired by Neural Turing Machine (NTM), stores processed temporal relations in narrative order, and retrieves them for use when relevant entities come in. Relations are then classified in context. The GCL model has long-term memory and attention mechanisms to resolve irregular long-distance dependencies that regular RNNs such as LSTM cannot recognize. It does not require any new input features, while outperforming the existing models in literature. To our knowledge it is also the first model to use NTM-like architecture to process the information from global context in discourse-scale natural text processing. We are going to release the source code in the future.
Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis
Authors: Laurent Vanni, Melanie Ducoffe, Carlos Aguilar, Frederic Precioso, Damon Mayaffre
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, text-classification, Text Classification

In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the efficiency of our Text Deconvolution Saliency on corpora from three different languages: English, French, and Latin. For every tested dataset, our Text Deconvolution Saliency automatically encodes complex linguistic patterns based on co-occurrences and possibly on grammatical and syntax analysis.
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis
Authors: Tomohide Shibata, Sadao Kurohashi
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: coreference-resolution, Coreference Resolution, Reading Comprehension

Predicate argument structure analysis is a task of identifying structured events. To improve this field, we need to identify a salient entity, which cannot be identified without performing coreference resolution and predicate argument structure analysis simultaneously. This paper presents an entity-centric joint model for Japanese coreference resolution and predicate argument structure analysis. Each entity is assigned an embedding, and when the result of both analyses refers to an entity, the entity embedding is updated. The analyses take the entity embedding into consideration to access the global information of entities. Our experimental results demonstrate the proposed method can improve the performance of the inter-sentential zero anaphora resolution drastically, which is a notoriously difficult task in predicate argument structure analysis.
Constraining MGbank: Agreement, L-Selection and Supertagging in Minimalist Grammars
Authors: John Torr
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

This paper reports on two strategies that have been implemented for improving the efficiency and precision of wide-coverage Minimalist Grammar (MG) parsing. The first extends the formalism presented in Torr and Stabler (2016) with a mechanism for enforcing fine-grained selectional restrictions and agreements. The second is a method for factoring computationally costly null heads out from bottom-up MG parsing; this has the additional benefit of rendering the formalism fully compatible for the first time with highly efficient Markovian supertaggers. These techniques aided in the task of generating MGbank, the first wide-coverage corpus of Minimalist Grammar derivation trees.
Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power
Authors: Yang Xu, Jeremy Cole, David Reitter
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

Linguistic alignment between dialogue partners has been claimed to be affected by their relative social power. A common finding has been that interlocutors of higher power tend to receive more alignment than those of lower power. However, these studies overlook some low-level linguistic features that can also affect alignment, which casts doubts on these findings. This work characterizes the effect of power on alignment with logistic regression models in two datasets, finding that the effect vanishes or is reversed after controlling for low-level features such as utterance length. Thus, linguistic alignment is explained better by low-level features than by social power. We argue that a wider range of factors, especially cognitive factors, need to be taken into account for future studies on observational data when social factors of language use are in question.
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays
Authors: Winston Carlile, Nishant Gurrapadi, Zixuan Ke, Vincent Ng
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Automated Essay Scoring, Persuasiveness

While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research. Progress on scoring argument persuasiveness is hindered in part by the scarcity of annotated corpora. We present the first corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument{'}s persuasiveness. This corpus could trigger the development of novel computational models concerning argument persuasiveness that provide useful feedback to students on why their arguments are (un)persuasive in addition to how persuasive they are.
Inherent Biases in Reference-based Evaluation for Grammatical Error Correction
Authors: Leshem Choshen, Omri Abend
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Grammatical Error Correction, Sentence, Text Generation, Text Simplification, valid

The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB). This paper shows that overcoming LCB in Grammatical Error Correction (GEC) evaluation cannot be attained by re-scaling or by increasing the number of references in any feasible range, contrary to previous suggestions. This is due to the long-tailed distribution of valid corrections for a sentence. Concretely, we show that LCB incentivizes GEC systems to avoid correcting even when they can generate a valid correction. Consequently, existing systems obtain comparable or superior performance compared to humans, by making few but targeted changes to the input. Similar effects on Text Simplification further support our claims.
The price of debiasing automatic metrics in natural language evalaution
Authors: Arun Chaganty, Stephen Mussmann, Percy Liang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Image Captioning, Question Answering

For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias against certain model improvements. On the other hand, averaging human judgments, the unbiased gold standard, is often too expensive. In this paper, we use control variates to combine automatic metrics with human evaluation to obtain an unbiased estimator with lower cost than human evaluation alone. In practice, however, we obtain only a 7-13{\%} cost reduction on evaluating summarization and open-response question answering systems. We then prove that our estimator is optimal: there is no unbiased estimator with lower cost. Our theory further highlights the two fundamental bottlenecks{---}the automatic metric and the prompt shown to human evaluators{---}both of which need to be improved to obtain greater cost savings.
Modeling and Prediction of Online Product Review Helpfulness: A Survey
Authors: Gerardo Ocampo Diaz, Vincent Ng
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Recommendation Systems, Survey

As the amount of free-form user-generated reviews in e-commerce websites continues to increase, there is an increasing need for automatic mechanisms that sift through the vast amounts of user reviews and identify quality content. Review helpfulness modeling is a task which studies the mechanisms that affect review helpfulness and attempts to accurately predict it. This paper provides an overview of the most relevant work in helpfulness prediction and understanding in the past decade, discusses the insights gained from said work, and provides guidelines for future research.
Mining Cross-Cultural Differences and Similarities in Social Media
Authors: Bill Yuchen Lin, Frank F. Xu, Kenny Zhu, Seung-won Hwang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Natural Language Understanding, Sentiment Analysis, Translation

Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-cultural similarities. In this paper, we study the problem of computing such cross-cultural differences and similarities. We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Experimental results show that our framework substantially outperforms a number of baseline methods on both tasks. The framework could be useful for machine translation applications and research in computational social science.
Classification of Moral Foundations in Microblog Political Discourse
Authors: Kristen Johnson, Dan Goldwasser
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Classification, General Classification

Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.