pwc-archive/papers-with-abstracts huggingface dataset:
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization
Authors: Manling Li, Lingyu Zhang, Heng Ji, Richard J. Radke
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Articles, Meeting Summarization, Text Generation

Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention across three levels: segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional text features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that the utterance is more important if the speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation
Authors: Francine Chen, Yan-Ying Chen
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Abstractive Text Summarization, Articles, Decoder, Domain Adaptation, Text Generation

A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
Authors: Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, Iryna Gurevych
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Abstractive Text Summarization, Natural Language Inference, Reranking

While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice. In this paper, we evaluate summaries produced by state-of-the-art models via crowdsourcing and show that such errors occur frequently, in particular with more abstractive models. We study whether textual entailment predictions can be used to detect such errors and if they can be reduced by reranking alternative predicted summaries. That leads to an interesting downstream application for entailment models. In our experiments, we find that out-of-the-box entailment models trained on NLI datasets do not yet offer the desired performance for the downstream task and we therefore release our annotations as additional test data for future extrinsic evaluations of NLI.
On the Summarization of Consumer Health Questions
Authors: Asma Ben Abacha, Dina Demner-Fushman
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Data Augmentation, Question Answering, Retrieval

Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16{\%}. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.
Unsupervised Rewriter for Multi-Sentence Compression
Authors: Yang Zhao, Xiaoyu Shen, Wei Bi, Akiko Aizawa
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Sentence, Sentence Compression

Multi-sentence compression (MSC) aims to generate a grammatical but reduced compression from multiple input sentences while retaining their key information. Previous dominating approach for MSC is the extraction-based word graph approach. A few variants further leveraged lexical substitution to yield more abstractive compression. However, two limitations exist. First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression. Second, lexical substitution is often inappropriate without the consideration of context information. To tackle the above-mentioned issues, we present a neural rewriter for multi-sentence compression that does not need any parallel corpus. Empirical studies have shown that our approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation. A parallel corpus with more than 140,000 (sentence group, compression) pairs is also constructed as a by-product for future research.
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text
Authors: Jianxing Yu, Zheng-Jun Zha, Jian Yin
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Reading Comprehension, Reinforcement Learning

This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. In particular, we first encode the given document, question and options in a context aware way. We then propose a new network to solve the inference problem by decomposing it into a series of attention-based reasoning steps. The result of the previous step acts as the context of next step. To make each step can be directly inferred from the text, we design an operational cell with prior structure. By recursively linking the cells, the inferred results are synthesized together to form the evidence chain for reasoning, where the reasoning direction can be guided by imposing structural constraints to regulate interactions on the cells. Moreover, a termination mechanism is introduced to dynamically determine the uncertain reasoning depth, and the network is trained by reinforcement learning. Experimental results on 3 popular data sets, including MCTest, RACE and MultiRC, demonstrate the effectiveness of our approach.
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension
Authors: Yimeng Zhuang, Huadong Wang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Reading Comprehension

Multi-passage reading comprehension requires the ability to combine cross-passage information and reason over multiple passages to infer the answer. In this paper, we introduce the Dynamic Self-attention Network (DynSAN) for multi-passage reading comprehension task, which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. The core module of the dynamic self-attention is a proposed gated token selection mechanism, which dynamically selects important tokens from a sequence. These chosen tokens will attend to each other via a self-attention mechanism to model long-range dependencies. Besides, convolutional layers are combined with the dynamic self-attention to enhance the model{'}s capacity of extracting local semantic. The experimental results show that the proposed DynSAN achieves new state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets. Further ablation study also validates the effectiveness of our model components.
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
Authors: An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Reading Comprehension, Reading Comprehension

Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).
XQA: A Cross-lingual Open-domain Question Answering Dataset
Authors: Jiahua Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, Open-Domain Question Answering, Question Answering, Reading Comprehension, Retrieval, Text Retrieval

Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension. Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA. However, the success of these models relies on a massive volume of training data (usually in English), which is not available in many other languages, especially for those low-resource languages. Therefore, it is essential to investigate cross-lingual OpenQA. In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. It consists of a training set in English as well as development and test sets in eight other languages. Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method (multilingual BERT). Experimental results show that the multilingual BERT model achieves the best results in almost all target languages, while the performance of cross-lingual OpenQA is still much lower than that of English. Our analysis indicates that the performance of cross-lingual OpenQA is related to not only how similar the target language and English are, but also how difficult the question set of the target language is. The XQA dataset is publicly available at http://github.com/thunlp/XQA.
Semi-supervised Domain Adaptation for Dependency Parsing
Authors: Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, Luo Si
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Chinese Dependency Parsing, Dependency Parsing, Domain Adaptation, Multi-Task Learning, Semi-supervised Domain Adaptation, Unsupervised Domain Adaptation

During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.
Unsupervised Learning of PCFGs with Normalizing Flow
Authors: Lifeng Jin, Finale Doshi-Velez, Timothy Miller, Lane Schwartz, William Schuler
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Acquisition

Unsupervised PCFG inducers hypothesize sets of compact context-free rules as explanations for sentences. PCFG induction not only provides tools for low-resource languages, but also plays an important role in modeling language acquisition (Bannard et al., 2009; Abend et al. 2017). However, current PCFG induction models, using word tokens as input, are unable to incorporate semantics and morphology into induction, and may encounter issues of sparse vocabulary when facing morphologically rich languages. This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information. Linguistically motivated sparsity and categorical distance constraints are imposed on the inducer as regularization. Experiments show that the PCFG induction model with normalizing flow produces grammars with state-of-the-art accuracy on a variety of different languages. Ablation further shows a positive effect of normalizing flow, context embeddings and proposed regularizers.
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction
Authors: Lifeng Jin, William Schuler
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Model Selection

In unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs. In order to find a better indicator for quality of induced grammars, this paper correlates several linguistically- and psycholinguistically-motivated predictors to parsing accuracy on a large multilingual grammar induction evaluation data set. Results show that variance of average surprisal (VAS) better correlates with parsing accuracy than data likelihood and that using VAS instead of data likelihood for model selection provides a significant accuracy boost. Further evidence shows VAS to be a better candidate than data likelihood for predicting word order typology classification. Analyses show that VAS seems to separate content words from function words in natural language grammars, and to better arrange words with different frequencies into separate classes that are more consistent with linguistic theory.
Cross-Domain NER using Cross-Domain Language Modeling
Authors: Chen Jia, Xiaobo Liang, Yue Zhang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Cross-Domain Named Entity Recognition, Domain Adaptation, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning, Unsupervised Domain Adaptation

Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.
Graph-based Dependency Parsing with Graph Neural Networks
Authors: Tao Ji, Yuanbin Wu, Man Lan
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Dependency Parsing

We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN{'}s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0{\%}, 94.3{\%}) among systems without using any external resources.
Wide-Coverage Neural A* Parsing for Minimalist Grammars
Authors: John Torr, Milos Stanojevic, Mark Steedman, Shay B. Cohen
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Sentence

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky{'}s (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is cubic in the length of the sentence. The parser is publicly available.
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model
Authors: Yitao Cai, Huiyu Cai, Xiaojun Wan
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Attribute, Sarcasm Detection

Sarcasm is a subtle form of language in which people express the opposite of what is implied. Previous works of sarcasm detection focused on texts. However, more and more social media platforms like Twitter allow users to create multi-modal messages, including texts, images, and videos. It is insufficient to detect sarcasm from multi-model messages based only on texts. In this paper, we focus on multi-modal sarcasm detection for tweets consisting of texts and images in Twitter. We treat text features, image features and image attributes as three modalities and propose a multi-modal hierarchical fusion model to address this task. Our model first extracts image features and attribute features, and then leverages attribute features and bidirectional LSTM network to extract text features. Features of three modalities are then reconstructed and fused into one feature vector for prediction. We create a multi-modal sarcasm detection dataset based on Twitter. Evaluation results on the dataset demonstrate the efficacy of our proposed model and the usefulness of the three modalities.
\#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media
Authors: Arijit Ghosh Chowdhury, Ramit Sawhney, Rajiv Ratn Shah, Debanjan Mahata
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being. The {\#}MeToo trend has led to people talking about personal experiences of harassment more openly. This work at- tempts to aggregate such experiences of sex- ual abuse to facilitate a better understanding of social media constructs and to bring about social change. It has been found that disclo- sure of abuse has positive psychological im- pacts. Hence, we contend that such informa- tion can leveraged to create better campaigns for social change by analyzing how users react to these stories and to obtain a better insight into the consequences of sexual abuse. We use a three part Twitter-Specific Social Media Lan- guage Model to segregate personal recollec- tions of sexual harassment from Twitter posts. An extensive comparison with state-of-the-art generic and specific models along with a de- tailed error analysis explores the merit of our proposed model.
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
Authors: Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Articles, Claim Verification, Sentence

Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer attends on sentences that can semantically infer the claim on top of the first attention; and 3) An output layer predicts the verdict based on the embedded evidence. Experimental results on three public benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
Authors: Chang Li, Dan Goldwasser
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents{'} social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.
Celebrity Profiling
Authors: Matti Wiegmann, Benno Stein, Martin Potthast
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Gender Prediction, Occupation prediction, Transfer Learning

Celebrities are among the most prolific users of social media, promoting their personas and rallying followers. This activity is closely tied to genuine writing samples, which makes them worthy research subjects in many respects, not least profiling. With this paper we introduce the Webis Celebrity Corpus 2019. For its construction the Twitter feeds of 71,706 verified accounts have been carefully linked with their respective Wikidata items, crawling both. After cleansing, the resulting profiles contain an average of 29,968 words per profile and up to 239 pieces of personal information. A cross-evaluation that checked the correct association of Twitter account and Wikidata item revealed an error rate of only 0.6{\%}, rendering the profiles highly reliable. Our corpus comprises a wide cross-section of local and global celebrities, forming a unique combination of scale, profile comprehensiveness, and label reliability. We further establish the state of the art{'}s profiling performance by evaluating the winning approaches submitted to the PAN gender prediction tasks in a transfer learning experiment. They are only outperformed by our own deep learning approach, which we also use to exemplify celebrity occupation prediction for the first time.