mtybilly/Instruct2DS huggingface dataset:
Bridging CNNs, RNNs, and Weighted Finite-State Machines
Authors: Sam Thomson, Noah A. Smith Roy Schwartz
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Igor Labutov, Tom Mitchell Shashank Srivastava
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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., \textquoteleftusually' vs \textquoteleftalways') 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.
Sentence-State LSTM for Text Representation
Authors: Qi Liu, Linfeng Song Yue Zhang
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.
Universal Language Model Fine-tuning for Text Classification
Authors: Sebastian Ruder Jeremy Howard
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Authors: Hinrich Schütze, Benjamin Roth Nina Poerner
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
Improving Text-to-SQL Evaluation Methodology
Authors: Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, Dragomir Radev Catherine Finegan-Dollak
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.
Semantic Parsing with Syntax- and Table-Aware SQL Generation
Authors: Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou Yibo Sun
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question- SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.
Multitask Parsing Across Semantic Representations
Authors: Omri Abend, Ari Rappoport Daniel Hershcovich
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.
Character-Level Models versus Morphology in Semantic Role Labeling
Authors: Mark Steedman Gözde Gül Şahin
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling (SRL). In this work, we train various types of SRL models that use word, character and morphology level information and analyze how performance of characters compare to words and morphology for several languages. We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-level models and their semantic capability.
AMR Parsing as Graph Prediction with Latent Alignment
Authors: Ivan Titov Chunchuan Lyu
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational autoencoding framework and a continuous relaxation of the discrete alignments. We show that joint modeling is preferable to using a pipeline of align and parse. The parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25).
Accurate SHRG-Based Semantic Parsing
Authors: Weiwei Sun, Xiaojun Wan Yufei Chen
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin Danqing Huang
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Shay B. Cohen, Mirella Lapata Jiangming Liu
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Authors: Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin Dinghan Shen
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Many deep learning architectures have been proposed to model the \textitcompositionality in text sequences, requiring substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Based upon this understanding, we propose two additional pooling strategies over learned word embeddings: (\textiti) a max-pooling operation for improved interpretability; and (\textitii) a hierarchical pooling operation, which preserves spatial ($n$-gram) information within text sequences. We present experiments on 17 datasets encompassing three tasks: (\textiti) (long) document classification; (\textitii) text sequence matching; and (\textitiii) short text tasks, including classification and tagging.
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Authors: Kevin Gimpel John Wieting
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: We describe ParaNMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the non-English side of a large parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks. To show its utility, we use ParaNMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase generation.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
Authors: Maarten Sap, Emily Allaway, Noah A. Smith, Yejin Choi Hannah Rashkin
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event`s participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people`s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.
Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis
Authors: Daisuke Kawahara, Sadao Kurohashi Shuhei Kurita
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
Authors: Ruihong Huang Prafulla Kumar Choubey
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
Authors: Weiran Xu, William Yang Wang Pengda Qin
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level. In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator. Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification. The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Authors: Daojian Zeng, Shizhu He, Kang Liu, Jun Zhao Xiangrong Zeng
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu Yu Hong
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Anna Rumshisky Yuanliang Meng
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
Temporal Event Knowledge Acquisition via Identifying Narratives
Authors: Ruihong Huang Wenlin Yao
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal “before/after” event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperforms several recent neural network models on the narrative cloze task.
Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis
Authors: Melanie Ducoffe, Carlos Aguilar, Frederic Precioso, Damon Mayaffre Laurent Vanni
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach
Authors: Muhammad Tasnim Mohiuddin, Dat Tien Nguyen Shafiq Joty
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: We propose a novel coherence model for written asynchronous conversations (e.g., forums, emails), and show its applications in coherence assessment and thread reconstruction tasks. We conduct our research in two steps. First, we propose improvements to the recently proposed neural entity grid model by lexicalizing its entity transitions. Then, we extend the model to asynchronous conversations by incorporating the underlying conversational structure in the entity grid representation and feature computation. Our model achieves state of the art results on standard coherence assessment tasks in monologue and conversations outperforming existing models. We also demonstrate its effectiveness in reconstructing thread structures.
Deep Reinforcement Learning for Chinese Zero Pronoun Resolution
Authors: Yu Zhang, Wei-Nan Zhang, Ting Liu, William Yang Wang Qingyu Yin
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Recent neural network models for Chinese zero pronoun resolution gain great performance by capturing semantic information for zero pronouns and candidate antecedents, but tend to be short-sighted, operating solely by making local decisions. They typically predict coreference links between the zero pronoun and one single candidate antecedent at a time while ignoring their influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is crucial for classifying later zero pronoun-candidate antecedent pairs, a need which leads traditional models of zero pronoun resolution to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to deal with the task. With the help of the reinforcement learning agent, our system learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 show that our approach substantially outperforms the state-of-the-art methods under three experimental settings.
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis
Authors: Sadao Kurohashi Tomohide Shibata
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Jeremy Cole, David Reitter Yang Xu
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
Authors: Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, Dragomir Radev Alexander Fabbri
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 5,600 resources on NLP as well as the related fields of Artificial Intelligence (AI), Machine Learning (ML) and Information Retrieval (IR). Our dataset is notably the largest manually-picked corpus of resources intended for NLP education which does not include only academic papers. Additionally, we have created both a search engine and a command-line tool for the resources and have annotated the corpus to include lists of research topics, relevant resources for each topic, prerequisite relations among topics, relevant sub-parts of individual resources, among other annotations. We are releasing the dataset and present several avenues for further research.
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays
Authors: Nishant Gurrapadi, Zixuan Ke, Vincent Ng Winston Carlile
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Omri Abend Leshem Choshen
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Stephen Mussmann, Percy Liang Arun Chaganty
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
Neural Document Summarization by Jointly Learning to Score and Select Sentences
Authors: Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, Tiejun Zhao Qingyu Zhou
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. It first reads the document sentences with a hierarchical encoder to obtain the representation of sentences. Then it builds the output summary by extracting sentences one by one. Different from previous methods, our approach integrates the selection strategy into the scoring model, which directly predicts the relative importance given previously selected sentences. Experiments on the CNN/Daily Mail dataset show that the proposed framework significantly outperforms the state-of-the-art extractive summarization models.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
Authors: Wensi Ding, Zekun Zhang, Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis, Jean-Pierre Lorré Guokan Shang
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the strengths of multiple recent approaches while addressing their weaknesses. Moreover, we leverage recent advances in word embeddings and graph degeneracy applied to NLP to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures. Experiments on the AMI and ICSI corpus show that our system improves on the state-of-the-art. Code and data are publicly available, and our system can be interactively tested.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
Authors: Mohit Bansal Yen-Chun Chen
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC-2002 dataset, where we achieve higher scores than a state-of-the-art model.
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation
Authors: Ramakanth Pasunuru, Mohit Bansal Han Guo
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of-the-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model`s learned saliency and entailment skills.
Modeling and Prediction of Online Product Review Helpfulness: A Survey
Authors: Vincent Ng Gerardo Ocampo Diaz
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Frank F. Xu, Kenny Zhu, Seung-won Hwang Bill Yuchen Lin
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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: Dan Goldwasser Kristen Johnson
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: 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.
Coarse-to-Fine Decoding for Neural Semantic Parsing
Authors: Mirella Lapata Li Dong
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.
Confidence Modeling for Neural Semantic Parsing
Authors: Chris Quirk, Mirella Lapata Li Dong
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions are likely to be correct. Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying on attention scores.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Authors: Chunting Zhou, Junxian He, Graham Neubig Pengcheng Yin
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2018

Abstract: Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.