pwc-archive/papers-with-abstracts huggingface dataset:
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited
Authors: Artur Kulmizev, Miryam de Lhoneux, Johannes Gontrum, Elena Fano, Joakim Nivre
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Dependency Parsing, global-optimization, Sentence, Word Embeddings

Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope. In this paper, we show that, even though some details of the picture have changed after the switch to neural networks and continuous representations, the basic trade-off between rich features and global optimization remains essentially the same. Moreover, we show that deep contextualized word embeddings, which allow parsers to pack information about global sentence structure into local feature representations, benefit transition-based parsers more than graph-based parsers, making the two approaches virtually equivalent in terms of both accuracy and error profile. We argue that the reason is that these representations help prevent search errors and thereby allow transition-based parsers to better exploit their inherent strength of making accurate local decisions. We support this explanation by an error analysis of parsing experiments on 13 languages.
Learning with Limited Data for Multilingual Reading Comprehension
Authors: Kyungjae Lee, Sunghyun Park, Hojae Han, Jinyoung Yeo, Seung-won Hwang, Juho Lee
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Question Answering, Reading Comprehension, Translation

This paper studies the problem of supporting question answering in a new language with limited training resources. As an extreme scenario, when no such resource exists, one can (1) transfer labels from another language, and (2) generate labels from unlabeled data, using translator and automatic labeling function respectively. However, these approaches inevitably introduce noises to the training data, due to translation or generation errors, which require a judicious use of data with varying confidence. To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training. On reading comprehension task, we demonstrate the effectiveness of our model on low-resource languages with varying similarity to English, namely, Korean and French.
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts
Authors: S Pezzelle, ro, Raquel Fern{\'a}ndez
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

This work aims at modeling how the meaning of gradable adjectives of size ({`}big{'}, {`}small{'}) can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is {`}big{'} or {`}small{'} in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as {`}fixed{'} attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks. Crucially, models fail in developing abstract representations of gradable adjectives that can be used compositionally.
What Gets Echoed? Understanding the ``Pointers'' in Explanations of Persuasive Arguments
Authors: David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasive. We propose a novel word-level prediction task to investigate how explanations selectively reuse, or echo, information from what is being explained (henceforth, explanandum). We develop features to capture the properties of a word in the explanandum, and show that our proposed features not only have relatively strong predictive power on the echoing of a word in an explanation, but also enhance neural methods of generating explanations. In particular, while the non-contextual properties of a word itself are more valuable for stopwords, the interaction between the constituent parts of an explanandum is crucial in predicting the echoing of content words. We also find intriguing patterns of a word being echoed. For example, although nouns are generally less likely to be echoed, subjects and objects can, depending on their source, be more likely to be echoed in the explanations.
Modeling Frames in Argumentation
Authors: Yamen Ajjour, Milad Alshomary, Henning Wachsmuth, Benno Stein
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Clustering

In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the {``}right{''} frame(s) to convince the audience to adopt the author{'}s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience{'}s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates{'} topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.
Evaluating adversarial attacks against multiple fact verification systems
Authors: James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Fact Verification

Automated fact verification has been progressing owing to advancements in modeling and availability of large datasets. Due to the nature of the task, it is critical to understand the vulnerabilities of these systems against adversarial instances designed to make them predict incorrectly. We introduce two novel scoring metrics, attack potency and system resilience which take into account the correctness of the adversarial instances, an aspect often ignored in adversarial evaluations. We consider six fact verification systems from the recent Fact Extraction and VERification (FEVER) challenge: the four best-scoring ones and two baselines. We evaluate adversarial instances generated by a recently proposed state-of-the-art method, a paraphrasing method, and rule-based attacks devised for fact verification. We find that our rule-based attacks have higher potency, and that while the rankings among the top systems changed, they exhibited higher resilience than the baselines.
Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials
Authors: Wonsuk Yang, Seungwon Yoon, Ada Carpenter, Jong Park
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Attribute

Annotation quality control is a critical aspect for building reliable corpora through linguistic annotation. In this study, we present a simple but powerful quality control method using two-step reason selection. We gathered sentential annotations of local acceptance and three related attributes through a crowdsourcing platform. For each attribute, the reason for the choice of the attribute value is selected in a two-step manner. The options given for reason selection were designed to facilitate the detection of a nonsensical reason selection. We assume that a sentential annotation that contains a nonsensical reason is less reliable than the one without such reason. Our method, based solely on this assumption, is found to retain the annotations with satisfactory quality out of the entire annotations mixed with those of low quality.
A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing
Authors: Zeyu Dai, Ruihong Huang
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Discourse Parsing

We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing. Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations. Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function. Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.
Weakly Supervised Multilingual Causality Extraction from Wikipedia
Authors: Chikara Hashimoto
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Articles

We present a method for extracting causality knowledge from Wikipedia, such as Protectionism -{\textgreater} Trade war, where the cause and effect entities correspond to Wikipedia articles. Such causality knowledge is easy to verify by reading corresponding Wikipedia articles, to translate to multiple languages through Wikidata, and to connect to knowledge bases derived from Wikipedia. Our method exploits Wikipedia article sections that describe causality and the redundancy stemming from the multilinguality of Wikipedia. Experiments showed that our method achieved precision and recall above 98{\%} and 64{\%}, respectively. In particular, it could extract causalities whose cause and effect were written distantly in a Wikipedia article. We have released the code and data for further research.
Attribute-aware Sequence Network for Review Summarization
Authors: Junjie Li, Xuepeng Wang, Dawei Yin, Cheng-qing Zong
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Attribute, Decoder

Review summarization aims to generate a condensed summary for a review or multiple reviews. Existing review summarization systems mainly generate summary only based on review content and neglect the authors{'} attributes (e.g., gender, age, and occupation). In fact, when summarizing a review, users with different attributes usually pay attention to specific aspects and have their own word-using habits or writing styles. Therefore, we propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users{'} characteristics into account, which includes three modules: an attribute encoder encodes the attribute preferences over the words; an attribute-aware review encoder adopts an attribute-based selective mechanism to select the important information of a review; and an attribute-aware summary decoder incorporates attribute embedding and attribute-specific word-using habits into word prediction. To validate our model, we collect a new dataset TripAtt, comprising 495,440 attribute-review-summary triplets with three kinds of attribute information: gender, age, and travel status. Extensive experiments show that ASN achieves state-of-the-art performance on review summarization in both auto-metric ROUGE and human evaluation.
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Authors: Shuang Chen, Jinpeng Wang, Xiaocheng Feng, Feng Jiang, Bing Qin, Chin-Yew Lin
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Data-to-Text Generation, Text Generation

Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge. They often assume that writing knowledge can be acquired from the training data alone. However, when people are writing, they not only rely on the data but also consider related knowledge. In this paper, we enhance neural data-to-text models with external knowledge in a simple but effective way to improve the fidelity of generated text. Besides relying on parallel data and text as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words. This allows the model to infer relevant facts which are not explicitly stated in the data table from an external knowledge source. Experimental results on twenty-one Wikipedia infobox-to-text datasets show our model, KBAtt, consistently improves a state-of-the-art model on most of the datasets. In addition, to quantify when and why external knowledge is effective, we design a metric, KBGain, which shows a strong correlation with the observed performance boost. This result demonstrates the relevance of external knowledge and sparseness of original data are the main factors affecting system performance.
Reading Like HER: Human Reading Inspired Extractive Summarization
Authors: Ling Luo, Xiang Ao, Yan Song, Feiyang Pan, Min Yang, Qing He
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decision Making, Extractive Summarization, Extractive Text Summarization, Text Summarization

In this work, we re-examine the problem of extractive text summarization for long documents. We observe that the process of extracting summarization of human can be divided into two stages: 1) a rough reading stage to look for sketched information, and 2) a subsequent careful reading stage to select key sentences to form the summary. By simulating such a two-stage process, we propose a novel approach for extractive summarization. We formulate the problem as a contextual-bandit problem and solve it with policy gradient. We adopt a convolutional neural network to encode gist of paragraphs for rough reading, and a decision making policy with an adapted termination mechanism for careful reading. Experiments on the CNN and DailyMail datasets show that our proposed method can provide high-quality summaries with varied length, and significantly outperform the state-of-the-art extractive methods in terms of ROUGE metrics.
Surface Realisation Using Full Delexicalisation
Authors: Anastasia Shimorina, Claire Gardent
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Morphological Inflection, Sentence

Surface realisation (SR) maps a meaning representation to a sentence and can be viewed as consisting of three subtasks: word ordering, morphological inflection and contraction generation (e.g., clitic attachment in Portuguese or elision in French). We propose a modular approach to surface realisation which models each of these components separately, and evaluate our approach on the 10 languages covered by the SR{'}18 Surface Realisation Shared Task shallow track. We provide a detailed evaluation of how word order, morphological realisation and contractions are handled by the model and an analysis of the differences in word ordering performance across languages.
An End-to-End Generative Architecture for Paraphrase Generation
Authors: Qian Yang, Zhouyuan Huo, Dinghan Shen, Yong Cheng, Wenlin Wang, Guoyin Wang, Lawrence Carin
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Paraphrase Generation

Generating high-quality paraphrases is a fundamental yet challenging natural language processing task. Despite the effectiveness of previous work based on generative models, there remain problems with exposure bias in recurrent neural networks, and often a failure to generate realistic sentences. To overcome these challenges, we propose the first end-to-end conditional generative architecture for generating paraphrases via adversarial training, which does not depend on extra linguistic information. Extensive experiments on four public datasets demonstrate the proposed method achieves state-of-the-art results, outperforming previous generative architectures on both automatic metrics (BLEU, METEOR, and TER) and human evaluations.
Subtopic-driven Multi-Document Summarization
Authors: Xin Zheng, Aixin Sun, Jing Li, Karthik Muthuswamy
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Articles, Document Summarization, Multi-Document Summarization, Sentence

In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper. That is, the underlying topic can be collectively represented by all the documents in the set. Meanwhile, different documents may cover various different subtopics and the same subtopic can be across several documents. Inspired by topic model, the underlying topic of a document set can also be viewed as a collection of different subtopics of different importance. In this paper, we propose a summarization model called STDS. The model generates the underlying topic representation from both document view and subtopic view in parallel. The learning objective is to minimize the distance between the representations learned from the two views. The contextual information is encoded through a hierarchical RNN architecture. Sentence salience is estimated in a hierarchical way with subtopic salience and relative sentence salience, by considering the contextual information. Top ranked sentences are then extracted as a summary. Note that the notion of subtopic enables us to bring in additional information (e.g. comments to news articles) that is helpful for document summarization. Experimental results show that the proposed solution outperforms state-of-the-art methods on benchmark datasets.
Exploring Diverse Expressions for Paraphrase Generation
Authors: Lihua Qian, Lin Qiu, Wei-Nan Zhang, Xin Jiang, Yong Yu
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Diversity, Information Retrieval, Paraphrase Generation, Question Answering, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Sentence

Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification. Recently, neural generative models have shown promising results in paraphrase generation. However, prior work mainly focused on single paraphrase generation, while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. Few works have been done to solve diverse paraphrase generation. In this paper, we propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. A reinforcement learning algorithm is applied to train our model. Our experiments on two real-world datasets demonstrate that our model not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning
Authors: Toru Nishino, Shotaro Misawa, Ryuji Kano, Tomoki Taniguchi, Yasuhide Miura, Tomoko Ohkuma
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Abstractive Text Summarization, Articles, Document Classification, General Classification, Multi-Task Learning, Sentence

The automated generation of information indicating the characteristics of articles such as headlines, key phrases, summaries and categories helps writers to alleviate their workload. Previous research has tackled these tasks using neural abstractive summarization and classification methods. However, the outputs may be inconsistent if they are generated individually. The purpose of our study is to generate multiple outputs consistently. We introduce a multi-task learning model with a shared encoder and multiple decoders for each task. We propose a novel loss function called hierarchical consistency loss to maintain consistency among the attention weights of the decoders. To evaluate the consistency, we employ a human evaluation. The results show that our model generates more consistent headlines, key phrases and categories. In addition, our model outperforms the baseline model on the ROUGE scores, and generates more adequate and fluent headlines.
Toward a Task of Feedback Comment Generation for Writing Learning
Authors: Ryo Nagata
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Comment Generation, Retrieval

In this paper, we introduce a novel task called feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. There has been almost no work on this task nor corpus annotated with feedback comments. We have taken the first step by creating learner corpora consisting of approximately 1,900 essays where all preposition errors are manually annotated with feedback comments. We have tested three baseline methods on the dataset, showing that a simple neural retrieval-based method sets a baseline performance with an F-measure of 0.34 to 0.41. Finally, we have looked into the results to explore what modifications we need to make to achieve better performance. We also have explored problems unaddressed in this work
Deep Copycat Networks for Text-to-Text Generation
Authors: Julia Ive, Pranava Madhyastha, Lucia Specia
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Automatic Post-Editing, Machine Translation, Text Generation, Text Simplification, Translation

Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output. We introduce Copycat, a transformer-based pointer network for such tasks which obtains competitive results in abstractive text summarisation and generates more abstractive summaries. We propose a further extension of this architecture for automatic post-editing, where generation is conditioned over two inputs (source language and machine translation), and the model is capable of deciding where to copy information from. This approach achieves competitive performance when compared to state-of-the-art automated post-editing systems. More importantly, we show that it addresses a well-known limitation of automatic post-editing - overcorrecting translations - and that our novel mechanism for copying source language words improves the results.
``Transforming'' Delete, Retrieve, Generate Approach for Controlled Text Style Transfer
Authors: Akhilesh Sudhakar, Bhargav Upadhyay, Arjun Maheswaran
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence, Style Transfer, Text Style Transfer

Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained language models as well as the Transformer. GST is a part of a larger {`}Delete Retrieve Generate{'} framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.