pwc-archive/papers-with-abstracts huggingface dataset:
Ant Colony System for Multi-Document Summarization
Authors: Asma Al-Saleh, Mohamed El Bachir Menai
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Document Summarization, Multi-Document Summarization, Text Summarization

This paper proposes an extractive multi-document summarization approach based on an ant colony system to optimize the information coverage of summary sentences. The implemented system was evaluated on both English and Arabic versions of the corpus of the Text Analysis Conference 2011 MultiLing Pilot by using ROUGE metrics. The evaluation results are promising in comparison to those of the participating systems. Indeed, our system achieved the best scores based on several ROUGE metrics.
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian
Authors: Anna Rogers, Alexey Romanov, Anna Rumshisky, Svitlana Volkova, Mikhail Gronas, Alex Gribov
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Active Learning, General Classification, Sentiment Analysis, Word Embeddings

This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{'} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.
A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis
Authors: Shuqin Gu, Lipeng Zhang, Yuexian Hou, Yin Song
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aspect-Based Sentiment Analysis (ABSA), Feature Engineering, Position, Sentence

Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU. PBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our proposed PBAN model.
Dynamic Feature Selection with Attention in Incremental Parsing
Authors: Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dependency Parsing, Dialogue Generation, feature selection, Image Captioning, Machine Translation, Text Summarization

One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context. In this work, we present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature selection. Our model learns, for example, which tokens should a parser focus on, to decide the next action. Our multilingual experiment shows its effectiveness across many languages. We also present an experiment with augmented test dataset and demon- strate it helps to understand the model{'}s behavior on locally ambiguous points.
Vocabulary Tailored Summary Generation
Authors: Kundan Krishna, Aniket Murhekar, Saumitra Sharma, Balaji Vasan Srinivasan
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstractive Text Summarization, Text Summarization

Neural sequence-to-sequence models have been successfully extended for summary generation.However, existing frameworks generate a single summary for a given input and do not tune the summaries towards any additional constraints/preferences. Such a tunable framework is desirable to account for linguistic preferences of the specific audience who will consume the summary. In this paper, we propose a neural framework to generate summaries constrained to a vocabulary-defined linguistic preferences of a target audience. The proposed method accounts for the generation context by tuning the summary words at the time of generation. Our evaluations indicate that the proposed approach tunes summaries to the target vocabulary while still maintaining a superior summary quality against a state-of-the-art word embedding based lexical substitution algorithm, suggesting the feasibility of the proposed approach. We demonstrate two applications of the proposed approach - to generate understandable summaries with simpler words, and readable summaries with shorter words.
Reading Comprehension with Graph-based Temporal-Casual Reasoning
Authors: Yawei Sun, Gong Cheng, Yuzhong Qu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dependency Parsing, Reading Comprehension

Complex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text based on dependencies, and rank answers by aligning event graphs. In particular, the alignments are constrained by graph-based reasoning to ensure temporal and causal agreement. Our focused approach self-adaptively complements existing solutions; it is automatically triggered only when applicable. Experiments on RACE and MCTest show that state-of-the-art methods are notably improved by using our approach as an add-on.
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains
Authors: Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Domain Adaptation, Sentiment Analysis, Sentiment Classification

Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are structural correspondence learning and autoencoders. However, they either require long training times or suffer greatly on highly divergent domains. Inspired by recent advances in cross-lingual sentiment analysis, we provide a novel perspective and cast the domain adaptation problem as an embedding projection task. Our model takes as input two mono-domain embedding spaces and learns to project them to a bi-domain space, which is jointly optimized to (1) project across domains and to (2) predict sentiment. We perform domain adaptation experiments on 20 source-target domain pairs for sentiment classification and report novel state-of-the-art results on 11 domain pairs, including the Amazon domain adaptation datasets and SemEval 2013 and 2016 datasets. Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains. Our code is available at https://github.com/jbarnesspain/domain{\_}blse
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation
Authors: Sixing Wu, Dawei Zhang, Ying Li, Xing Xie, Zhonghai Wu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstractive Text Summarization, Decoder, Machine Translation, Response Generation

Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis
Authors: Yicheng Zou, Tao Gui, Qi Zhang, Xuanjing Huang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

Attention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance. However, most existing attention models did not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. To achieve the above target, in this work, we propose a novel lexicon-based supervised attention model (LBSA), which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations. Compared with general attention models, our model has better interpretability and less noise. Experimental results on three large-scale sentiment classification datasets showed that the proposed method outperforms previous methods.
Open-Domain Event Detection using Distant Supervision
Authors: Jun Araki, Teruko Mitamura
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Event Detection, Open-Domain Question Answering, Question Answering

This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation. The goal is to detect all kinds of events regardless of domains. Given the absence of training data, we propose a distant supervision method that is able to generate high-quality training data. Using a manually annotated event corpus as gold standard, our experiments show that despite no direct supervision, the model outperforms supervised models. This result indicates that the distant supervision enables robust event detection in various domains, while obviating the need for human annotation of events.
Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing
Authors: Bo Chen, Bo An, Le Sun, Xianpei Han
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Semantic Parsing

Semantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. In this paper, we propose a graph-based semi-supervised learning framework that makes use of large text corpora and lexical resources. This framework first constructs a graph with a phrase similarity model learned by utilizing many text corpora and lexical resources. Next, graph propagation algorithm identifies the label distribution of unlabeled phrases from labeled ones. We evaluate our approach on two benchmarks: Webquestions and Free917. The results show that, in both datasets, our method achieves substantial improvement when comparing to the base system that does not utilize the learned lexicon, and gains competitive results when comparing to state-of-the-art systems.
Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings
Authors: Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Diversity, Informativeness, Text Summarization, Word Embeddings

We present a new summary evaluation approach that does not require human model summaries. Our approach exploits the compositional capabilities of corpus-based and lexical resource-based word embeddings to develop the features reflecting coverage, diversity, informativeness, and coherence of summaries. The features are then used to train a learning model for predicting the summary content quality in the absence of gold models. We evaluate the proposed metric in replicating the human assigned scores for summarization systems and summaries on data from query-focused and update summarization tasks in TAC 2008 and 2009. The results show that our feature combination provides reliable estimates of summary content quality when model summaries are not available.
A review of Spanish corpora annotated with negation
Authors: Salud Mar{\'\i}a Jim{\'e}nez-Zafra, Roser Morante, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Negation, Question Answering, Sentiment Analysis

The availability of corpora annotated with negation information is essential to develop negation processing systems in any language. However, there is a lack of these corpora even for languages like English, and when there are corpora available they are small and the annotations are not always compatible across corpora. In this paper we review the existing corpora annotated with negation in Spanish with the purpose of first, gathering the information to make it available for other researchers and, second, analyzing how compatible are the corpora and how has the linguistic phenomenon been addressed. Our final aim is to develop a supervised negation processing system for Spanish, for which we need training and test data. Our analysis shows that it will not be possible to merge the small corpora existing for Spanish due to lack of compatibility in the annotations.
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings
Authors: Junjie Li, Haitong Yang, Cheng-qing Zong
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Multi-Task Learning, Sentence, Sentiment Analysis, Sentiment Classification

Document-level multi-aspect sentiment classification aims to predict user{'}s sentiment polarities for different aspects of a product in a review. Existing approaches mainly focus on text information. However, the authors (i.e. users) and overall ratings of reviews are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects in this paper. Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly. Specifically, HUARN adopts a hierarchical architecture to encode word, sentence, and document level information. Then, user attention and aspect attention are introduced into building sentence and document level representation. The document representation is combined with user and overall rating information to predict aspect ratings of a review. Diverse aspects are treated differently and a multi-task framework is adopted. Empirical results on two real-world datasets show that HUARN achieves state-of-the-art performances.
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora
Authors: Amir Hazem, Emmanuel Morin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Machine Translation

Recent evaluations on bilingual lexicon extraction from specialized comparable corpora have shown contrasted performance while using word embedding models. This can be partially explained by the lack of large specialized comparable corpora to build efficient representations. Within this context, we try to answer the following questions: First, (i) among the state-of-the-art embedding models, whether trained on specialized corpora or pre-trained on large general data sets, which one is the most appropriate model for bilingual terminology extraction? Second (ii) is it worth it to combine multiple embeddings trained on different data sets? For that purpose, we propose the first systematic evaluation of different word embedding models for bilingual terminology extraction from specialized comparable corpora. We emphasize how the character-based embedding model outperforms other models on the quality of the extracted bilingual lexicons. Further more, we propose a new efficient way to combine different embedding models learned from specialized and general-domain data sets. Our approach leads to higher performance than the best individual embedding model.
Learning Emotion-enriched Word Representations
Authors: Ameeta Agrawal, Aijun An, Manos Papagelis
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Emotion Classification, General Classification, Multi-Label Classification, Representation Learning, Word Embeddings

Most word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar meanings. As a consequence, emotionally dissimilar words, such as {``}happy{''} and {``}sad{''} occurring in similar contexts would purport more similar meaning than emotionally similar words, such as {``}happy{''} and {``}joy{''}. This complication leads to rather undesirable outcome in predictive tasks that relate to affect (emotional state), such as emotion classification and emotion similarity. In order to address this limitation, we propose a novel method of obtaining emotion-enriched word representations, which projects emotionally similar words into neighboring spaces and emotionally dissimilar ones far apart. The proposed approach leverages distant supervision to automatically obtain a large training dataset of text documents and two recurrent neural network architectures for learning the emotion-enriched representations. Through extensive evaluation on two tasks, including emotion classification and emotion similarity, we demonstrate that the proposed representations outperform several competitive general-purpose and affective word representations.
Evaluating the text quality, human likeness and tailoring component of PASS: A Dutch data-to-text system for soccer
Authors: Chris van der Lee, Bart Verduijn, Emiel Krahmer, S Wubben, er
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Text Generation

We present an evaluation of PASS, a data-to-text system that generates Dutch soccer reports from match statistics which are automatically tailored towards fans of one club or the other. The evaluation in this paper consists of two studies. An intrinsic human-based evaluation of the system{'}s output is described in the first study. In this study it was found that compared to human-written texts, computer-generated texts were rated slightly lower on style-related text components (fluency and clarity) and slightly higher in terms of the correctness of given information. Furthermore, results from the first study showed that tailoring was accurately recognized in most cases, and that participants struggled with correctly identifying whether a text was written by a human or computer. The second study investigated if tailoring affects perceived text quality, for which no results were garnered. This lack of results might be due to negative preconceptions about computer-generated texts which were found in the first study.
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension
Authors: Mao Nakanishi, Tetsunori Kobayashi, Yoshihiko Hayashi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, valid

Machine-reading comprehension (MRC) has recently attracted attention in the fields of natural language processing and machine learning. One of the problematic presumptions with current MRC technologies is that each question is assumed to be answerable by looking at a given text passage. However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions. To develop this functionality, a dataset incorporating hard-to-detect NAQs is vital; however, its manual construction would be expensive. This paper proposes a dataset creation method that alters an existing MRC dataset, the Stanford Question Answering Dataset, and describes the resulting dataset. The value of this dataset is likely to increase if each NAQ in the dataset is properly classified with the difficulty of identifying it as an NAQ. This difficulty level would allow researchers to evaluate a machine{'}s NAQ detection performance more precisely. Therefore, we propose a method for automatically assigning difficulty level labels, which measures the similarity between a question and the target text passage. Our NAQ detection experiments demonstrate that the resulting dataset, having difficulty level annotations, is valid and potentially useful in the development of advanced MRC models.
Encoding Sentiment Information into Word Vectors for Sentiment Analysis
Authors: Zhe Ye, Fang Li, Timothy Baldwin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Learning Word Embeddings, Sentiment Analysis, Word Embeddings

General-purpose pre-trained word embeddings have become a mainstay of natural language processing, and more recently, methods have been proposed to encode external knowledge into word embeddings to benefit specific downstream tasks. The goal of this paper is to encode sentiment knowledge into pre-trained word vectors to improve the performance of sentiment analysis. Our proposed method is based on a convolutional neural network (CNN) and an external sentiment lexicon. Experiments on four popular sentiment analysis datasets show that this method improves the accuracy of sentiment analysis compared to a number of benchmark methods.
Towards a Language for Natural Language Treebank Transductions
Authors: Carlos A. Prolo
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper describes a transduction language suitable for natural language treebank transformations and motivates its application to tasks that have been used and described in the literature. The language, which is the basis for a tree transduction tool allows for clean, precise and concise description of what has been very confusingly, ambiguously, and incompletely textually described in the literature also allowing easy non-hard-coded implementation. We also aim at getting feedback from the NLP community to eventually converge to a de facto standard for such transduction language.
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training
Authors: Zhongyang Li, Xiao Ding, Ting Liu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Cloze Test, Information Retrieval, Story Generation

Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE). However, the pure MLE training objective much limits the power of Seq2Seq model in generating high-quality storys. In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context. Our model includes a generator that defines the policy of generating a story ending, and a discriminator that labels story endings as human-generated or machine-generated. Carefully designed human and automatic evaluation metrics demonstrate that our adversarial training augmented Seq2Seq model can generate more reasonable and diversified story endings compared to purely MLE-trained Seq2Seq model. Moreover, our model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6{\%} compared with state-of-the-art baseline methods.