pwc-archive/papers-with-abstracts huggingface dataset:
Seq2seq Dependency Parsing
Authors: Zuchao Li, Jiaxun Cai, Shexia He, Hai Zhao
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Dependency Parsing, Feature Engineering, Position

This paper presents a sequence to sequence (seq2seq) dependency parser by directly predicting the relative position of head for each given word, which therefore results in a truly end-to-end seq2seq dependency parser for the first time. Enjoying the advantage of seq2seq modeling, we enrich a series of embedding enhancement, including firstly introduced subword and node2vec augmentation. Meanwhile, we propose a beam search decoder with tree constraint and subroot decomposition over the sequence to furthermore enhance our seq2seq parser. Our parser is evaluated on benchmark treebanks, being on par with the state-of-the-art parsers by achieving 94.11{\%} UAS on PTB and 88.78{\%} UAS on CTB, respectively.
Neural Machine Translation Incorporating Named Entity
Authors: Arata Ugawa, Akihiro Tamura, Takashi Ninomiya, Hiroya Takamura, Manabu Okumura
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, NMT, Sentence, TAG, Translation

This study proposes a new neural machine translation (NMT) model based on the encoder-decoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models{'}abilitytotranslatecompoundwordsseemschallengingbecausetheencoderreceivesaword, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore,the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.
Semantic Parsing for Technical Support Questions
Authors: Abhirut Gupta, Anupama Ray, Gargi Dasgupta, Gautam Singh, Pooja Aggarwal, Prateeti Mohapatra
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Retrieval, Semantic Parsing

Technical support problems are very complex. In contrast to regular web queries (that contain few keywords) or factoid questions (which are a few sentences), these problems usually include attributes like a detailed description of what is failing (symptom), steps taken in an effort to remediate the failure (activity), and sometimes a specific request or ask (intent). Automating support is the task of automatically providing answers to these problems given a corpus of solution documents. Traditional approaches to this task rely on information retrieval and are keyword based; looking for keyword overlap between the question and solution documents and ignoring these attributes. We present an approach for semantic parsing of technical questions that uses grammatical structure to extract these attributes as a baseline, and a CRF based model that can improve performance considerably in the presence of annotated data for training. We also demonstrate that combined with reasoning, these attributes help outperform retrieval baselines.
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
Authors: Yanchao Hao, Hao liu, Shizhu He, Kang Liu, Jun Zhao
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Linking, Fact Selection, Question Answering, Retrieval

Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge to QA systems. In this work, we propose to conduct pattern extraction and entity linking first, and put forward pattern revising procedure to mitigate the error propagation problem. In order to learn to rank candidate subject-predicate pairs to enable the relevant facts retrieval given a question, we propose to do joint fact selection enhanced by relation detection. Multi-level encodings and multi-dimension information are leveraged to strengthen the whole procedure. The experimental results demonstrate that our approach sets a new record in this task, outperforming the current state-of-the-art by an absolute large margin.
Integrating Question Classification and Deep Learning for improved Answer Selection
Authors: Harish Tayyar Madabushi, Mark Lee, John Barnden
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Answer Selection, Classification, Deep Learning, General Classification, Question Answering

We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
Authors: Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Answer Selection, Information Retrieval, Question Answering, Reading Comprehension, Transfer Learning

Answer selection is an important but challenging task. Significant progresses have been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the knowledge-based representational learning into answer selection models. The learned knowledge-based representations are shared by source and target domains, which not only leverages large amounts of cross-domain data, but also benefits from a regularization effect that leads to more general representations to help tasks in new domains. To verify the effectiveness of our model, we use SQuAD-T dataset as the source domain and three other datasets (i.e., Yahoo QA, TREC QA and InsuranceQA) as the target domains. The experimental results demonstrate that KAN has remarkable applicability and generality, and consistently outperforms the strong competitors by a noticeable margin for cross-domain answer selection.
Rethinking the Agreement in Human Evaluation Tasks
Authors: Jacopo Amidei, Paul Piwek, Alistair Willis
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialogue Generation, Question Generation, Question-Generation, Text Generation

Human evaluations are broadly thought to be more valuable the higher the inter-annotator agreement. In this paper we examine this idea. We will describe our experiments and analysis within the area of Automatic Question Generation. Our experiments show how annotators diverge in language annotation tasks due to a range of ineliminable factors. For this reason, we believe that annotation schemes for natural language generation tasks that are aimed at evaluating language quality need to be treated with great care. In particular, an unchecked focus on reduction of disagreement among annotators runs the danger of creating generation goals that reward output that is more distant from, rather than closer to, natural human-like language. We conclude the paper by suggesting a new approach to the use of the agreement metrics in natural language generation evaluation tasks.
Can Rumour Stance Alone Predict Veracity?
Authors: Sebastian Dungs, Ahmet Aker, Norbert Fuhr, Kalina Bontcheva
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Rumour Detection, Veracity Classification

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets{'} times as the only features for modelling true and false rumours achieve F1 scores in the range of 80{\%}, outperforming those approaches where stance is used jointly with content and user based features.
Attending Sentences to detect Satirical Fake News
Authors: Sohan De Sarkar, Fan Yang, Arjun Mukherjee
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Misinformation, Satire Detection, Sentence, Word Embeddings

Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.
Predicting Stances from Social Media Posts using Factorization Machines
Authors: Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, Kentaro Inui
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decision Making, Stance Detection

Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. An important step to analyze the discussions on social media and to assist in healthy decision-making is stance detection. This paper presents an approach to detect the stance of a user toward a topic based on their stances toward other topics and the social media posts of the user. We apply factorization machines, a widely used method in item recommendation, to model user preferences toward topics from the social media data. The experimental results demonstrate that users{'} posts are useful to model topic preferences and therefore predict stances of silent users.
Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains.
Authors: Prathusha K Sarma, William Sethares
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis, Text Classification, Word Embeddings

Standard word embedding algorithms learn vector representations from large corpora of text documents in an unsupervised fashion. However, the quality of word embeddings learned from these algorithms is affected by the size of training data sets. Thus, applications of these algorithms in domains with only moderate amounts of available data is limited. In this paper we introduce an algorithm that learns word embeddings jointly with a classifier. Our algorithm is called SWESA (Supervised Word Embeddings for Sentiment Analysis). SWESA leverages document label information to learn vector representations of words from a modest corpus of text documents by solving an optimization problem that minimizes a cost function with respect to both word embeddings and the weight vector used for classification. Experiments on several real world data sets show that SWESA has superior performance on domains with limited data, when compared to previously suggested approaches to word embeddings and sentiment analysis tasks.
Personalized Text Retrieval for Learners of Chinese as a Foreign Language
Authors: Chak Yan Yeung, John Lee
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Active Learning, Complex Word Identification, Retrieval, Text Retrieval

This paper describes a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity. The user first rates their knowledge of a small set of words, chosen by a graph-based active learning model. The system trains a complex word identification model on this set, and then applies the model to find texts that contain the desired proportion of new, challenging, and familiar vocabulary. In an evaluation on learners of Chinese as a foreign language, we show that this algorithm is effective in identifying simpler texts for low-proficiency learners, and more challenging ones for high-proficiency learners.
Punctuation as Native Language Interference
Authors: Ilia Markov, Vivi Nastase, Carlo Strapparava
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Cross-corpus, General Classification, Language Identification, Multi-class Classification, Native Language Identification

In this paper, we describe experiments designed to explore and evaluate the impact of punctuation marks on the task of native language identification. Punctuation is specific to each language, and is part of the indicators that overtly represent the manner in which each language organizes and conveys information. Our experiments are organized in various set-ups: the usual multi-class classification for individual languages, also considering classification by language groups, across different proficiency levels, topics and even cross-corpus. The results support our hypothesis that punctuation marks are persistent and robust indicators of the native language of the author, which do not diminish in influence even when a high proficiency level in a non-native language is achieved.
Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning
Authors: Leonardo Zilio, Rodrigo Wilkens, C{\'e}drick Fairon
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Attribute, Language Acquisition

The literature frequently addresses the differences in receptive and productive vocabulary, but grammar is often left unacknowledged in second language acquisition studies. In this paper, we used two corpora to investigate the divergences in the behavior of pedagogically relevant grammatical structures in reception and production texts. We further improved the divergence scores observed in this investigation by setting a polarity to them that indicates whether there is overuse or underuse of a grammatical structure by language learners. This led to the compilation of a language profile that was later combined with vocabulary and readability features for classifying reception and production texts in three classes: beginner, intermediate, and advanced. The results of the automatic classification task in both production (0.872 of F-measure) and reception (0.942 of F-measure) were comparable to the current state of the art. We also attempted to automatically attribute a score to texts produced by learners, and the correlation results were encouraging, but there is still a good amount of room for improvement in this task. The developed language profile will serve as input for a system that helps language learners to activate more of their passive knowledge in writing texts.
MCDTB: A Macro-level Chinese Discourse TreeBank
Authors: Feng Jiang, Sheng Xu, Xiaomin Chu, Peifeng Li, Qiaoming Zhu, Guodong Zhou
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Reading Comprehension

In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Fol-lowing RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB.
Corpus-based Content Construction
Authors: Balaji Vasan Srinivasan, Pranav Maneriker, Kundan Krishna, Natwar Modani
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Diversity

Enterprise content writers are engaged in writing textual content for various purposes. Often, the text being written may already be present in the enterprise corpus in the form of past articles and can be re-purposed for the current needs. In the absence of suitable tools, authors manually curate/create such content (sometimes from scratch) which reduces their productivity. To address this, we propose an automatic approach to generate an initial version of the author{'}s intended text based on an input content snippet. Starting with a set of extracted textual fragments related to the snippet based on the query words in it, the proposed approach builds the desired text from these fragment by simultaneously optimizing the information coverage, relevance, diversity and coherence in the generated content. Evaluations on standard datasets shows improved performance against existing baselines on several metrics.
Bridging resolution: Task definition, corpus resources and rule-based experiments
Authors: Ina Roesiger, Arndt Riester, Jonas Kuhn
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Coreference Resolution

Recent work on bridging resolution has so far been based on the corpus ISNotes (Markert et al. 2012), as this was the only corpus available with unrestricted bridging annotation. Hou et al. 2014{'}s rule-based system currently achieves state-of-the-art performance on this corpus, as learning-based approaches suffer from the lack of available training data. Recently, a number of new corpora with bridging annotations have become available. To test the generalisability of the approach by Hou et al. 2014, we apply a slightly extended rule-based system to these corpora. Besides the expected out-of-domain effects, we also observe low performance on some of the in-domain corpora. Our analysis shows that this is the result of two very different phenomena being defined as bridging, namely referential and lexical bridging. We also report that filtering out gold or predicted coreferent anaphors before applying the bridging resolution system helps improve bridging resolution.
Semi-Supervised Disfluency Detection
Authors: Feng Wang, Wei Chen, Zhen Yang, Qianqian Dong, Shuang Xu, Bo Xu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Generative Adversarial Network, Machine Translation, Question Answering

While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.
Arrows are the Verbs of Diagrams
Authors: Malihe Alikhani, Matthew Stone
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, World Knowledge

Arrows are a key ingredient of schematic pictorial communication. This paper investigates the interpretation of arrows through linguistic, crowdsourcing and machine-learning methodology. Our work establishes a novel analogy between arrows and verbs: we advocate representing arrows in terms of qualitatively different structural and semantic frames, and resolving frames to specific interpretations using shallow world knowledge.
Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection
Authors: Olga Zamaraeva, Kristen Howell, Adam Rhine
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Negation, Negation Detection

We use a broad coverage, linguistically precise English Resource Grammar (ERG) to detect negation scope in sentences taken from pathology reports. We show that incorporating this information in feature extraction has a positive effect on classification of the reports with respect to cancer laterality compared with NegEx, a commonly used tool for negation detection. We analyze the differences between NegEx and ERG results on our dataset and how these differences indicate some directions for future work.
Bridge Video and Text with Cascade Syntactic Structure
Authors: Guolong Wang, Zheng Qin, Kaiping Xu, Kai Huang, Shuxiong Ye
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Attribute, Object, Region Proposal, Video Captioning, Video Description

We present a video captioning approach that encodes features by progressively completing syntactic structure (LSTM-CSS). To construct basic syntactic structure (i.e., subject, predicate, and object), we use a Conditional Random Field to label semantic representations (i.e., motions, objects). We argue that in order to improve the comprehensiveness of the description, the local features within object regions can be used to generate complementary syntactic elements (e.g., attribute, adverbial). Inspired by redundancy of human receptors, we utilize a Region Proposal Network to focus on the object regions. To model the final temporal dynamics, Recurrent Neural Network with Path Embeddings is adopted. We demonstrate the effectiveness of LSTM-CSS on generating natural sentences: 42.3{\%} and 28.5{\%} in terms of BLEU@4 and METEOR. Superior performance when compared to state-of-the-art methods are reported on a large video description dataset (i.e., MSR-VTT-2016).