pwc-archive/papers-with-abstracts huggingface dataset:
Topic or Style? Exploring the Most Useful Features for Authorship Attribution
Authors: Yunita Sari, Mark Stevenson, Andreas Vlachos
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Authorship Attribution, Text Categorization

Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals{'} writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.
A Deep Dive into Word Sense Disambiguation with LSTM
Authors: Minh Le, Marten Postma, Jacopo Urbani, Piek Vossen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling, Word Sense Disambiguation

LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code was released. This paper presents the results of a reproduction study and analysis of this technique using only openly available datasets (GigaWord, SemCor, OMSTI) and software (TensorFlow). Our study showed that similar results can be obtained with much less data than hinted at by Yuan et al. (2016). Detailed analyses shed light on the strengths and weaknesses of this method. First, adding more unannotated training data is useful, but is subject to diminishing returns. Second, the model can correctly identify both popular and unpopular meanings. Finally, the limited sense coverage in the annotated datasets is a major limitation. All code and trained models are made freely available.
Enriching Word Embeddings with Domain Knowledge for Readability Assessment
Authors: Zhiwei Jiang, Qing Gu, Yafeng Yin, Daoxu Chen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Word Embeddings

In this paper, we present a method which learns the word embedding for readability assessment. For the existing word embedding models, they typically focus on the syntactic or semantic relations of words, while ignoring the reading difficulty, thus they may not be suitable for readability assessment. Hence, we provide the knowledge-enriched word embedding (KEWE), which encodes the knowledge on reading difficulty into the representation of words. Specifically, we extract the knowledge on word-level difficulty from three perspectives to construct a knowledge graph, and develop two word embedding models to incorporate the difficulty context derived from the knowledge graph to define the loss functions. Experiments are designed to apply KEWE for readability assessment on both English and Chinese datasets, and the results demonstrate both effectiveness and potential of KEWE.
Authorship Identification for Literary Book Recommendations
Authors: Haifa Alharthi, Diana Inkpen, Stan Szpakowicz
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Collaborative Filtering, Recommendation Systems

Book recommender systems can help promote the practice of reading for pleasure, which has been declining in recent years. One factor that influences reading preferences is writing style. We propose a system that recommends books after learning their authors{'} style. To our knowledge, this is the first work that applies the information learned by an author-identification model to book recommendations. We evaluated the system according to a top-k recommendation scenario. Our system gives better accuracy when compared with many state-of-the-art methods. We also conducted a qualitative analysis by checking if similar books/authors were annotated similarly by experts.
A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese
Authors: Sidney Evaldo Leal, Magali Sanches Duran, S Alu{\'\i}sio, ra Maria
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentence, Triplet

Effective textual communication depends on readers being proficient enough to comprehend texts, and texts being clear enough to be understood by the intended audience, in a reading task. When the meaning of textual information and instructions is not well conveyed, many losses and damages may occur. Among the solutions to alleviate this problem is the automatic evaluation of sentence readability, task which has been receiving a lot of attention due to its large applicability. However, a shortage of resources, such as corpora for training and evaluation, hinders the full development of this task. In this paper, we generate a nontrivial sentence corpus in Portuguese. We evaluate three scenarios for building it, taking advantage of a parallel corpus of simplification, in which each sentence triplet is aligned and has simplification operations annotated, being ideal for justifying possible mistakes of future methods. The best scenario of our corpus PorSimplesSent is composed of 4,888 pairs, which is bigger than a similar corpus for English; all the three versions of it are publicly available. We created four baselines for PorSimplesSent and made available a pairwise ranking method, using 17 linguistic and psycholinguistic features, which correctly identifies the ranking of sentence pairs with an accuracy of 74.2{\%}.
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference
Authors: Qianlong Du, Cheng-qing Zong, Keh-Yih Su
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decision Making, Machine Translation, Natural Language Inference, Question Answering, Sentence, Text Summarization, Triplet

This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets. Most previous DNN-based approaches either ignore syntactic dependency among words, or directly use tree-LSTM to generate sentence representation with irrelevant information. To overcome the problems mentioned above, we adopt Word-Pair-Dependency-Triplets to improve alignment and inference judgment. To be specific, instead of comparing each triplet from one passage with the merged information of another passage, we first propose to perform comparison directly between the triplets of the given passage-pair to make the judgement more interpretable. Experimental results show that the performance of our approach is better than most of the approaches that use tree structures, and is comparable to other state-of-the-art approaches.
Cooperative Denoising for Distantly Supervised Relation Extraction
Authors: Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, Ying Shen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Denoising, Information Retrieval, Knowledge Distillation, Question Answering, Relation, Relation Extraction

Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.
Adversarial Feature Adaptation for Cross-lingual Relation Classification
Authors: Bowei Zou, Zengzhuang Xu, Yu Hong, Guodong Zhou
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Domain Adaptation, General Classification, Generative Adversarial Network, Knowledge Base Population, Question Answering, Relation, Relation Classification, Representation Learning, Sentence

Relation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods focus on exploiting mono-lingual data, e.g., in English, due to the lack of annotated data in other languages. In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data. Such a feature adaptation approach enables feature imitation via the competition between a relation classification network and a rival discriminator. Experimental results on the ACE 2005 multilingual training corpus, treating English as the source language and Chinese the target, demonstrate the effectiveness of our proposed approach, yielding an improvement of 5.7{\%} over the state-of-the-art.
Interpretation of Implicit Conditions in Database Search Dialogues
Authors: Shun-ya Fukunaga, Hitoshi Nishikawa, Takenobu Tokunaga, Hikaru Yokono, Tetsuro Takahashi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Targeting the database search dialogue, we propose to utilise information in the user utterances that do not directly mention the database (DB) field of the backend database system but are useful for constructing database queries. We call this kind of information implicit conditions. Interpreting the implicit conditions enables the dialogue system more natural and efficient in communicating with humans. We formalised the interpretation of the implicit conditions as classifying user utterances into the related DB field while identifying the evidence for that classification at the same time. Introducing this new task is one of the contributions of this paper. We implemented two models for this task: an SVM-based model and an RCNN-based model. Through the evaluation using a corpus of simulated dialogues between a real estate agent and a customer, we found that the SVM-based model showed better performance than the RCNN-based model.
Few-Shot Charge Prediction with Discriminative Legal Attributes
Authors: Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, Maosong Sun
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Attribute, Prediction, Text Classification

Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than 50{\%} in the few-shot scenario. Our codes and datasets can be obtained from https://github.com/thunlp/attribute{\_}charge.
Natural Language Interface for Databases Using a Dual-Encoder Model
Authors: Ionel Alex Hosu, Radu Cristian Alex Iacob, ru, Florin Brad, Stefan Ruseti, Traian Rebedea
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Semantic Parsing

We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user{'}s request in natural language. The sketch is obtained by using placeholders for specific entities in the SQL query, such as column names, table names, aliases and variables, in a process similar to semantic parsing. The first step is to apply a sequence-to-sequence (SEQ2SEQ) model to determine the most probable SQL sketch based on the request in natural language. Then, a second network designed as a dual-encoder SEQ2SEQ model using both the text query and the previously obtained sketch is employed to generate the final SQL query. Our approach shows improvements over previous approaches on two recent large datasets (WikiSQL and SENLIDB) suitable for data-driven solutions for natural language interfaces for databases.
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse
Authors: Sheng Xu, Peifeng Li, Guodong Zhou, Qiaoming Zhu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Question Answering, Text Matching

The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1.
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
Authors: Xiaomin Chu, Feng Jiang, Yi Zhou, Guodong Zhou, Qiaoming Zhu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Discourse Parsing, global-optimization, Machine Translation, Question Answering, Text Summarization

Discourse parsing is a challenging task and plays a critical role in discourse analysis. This paper focus on the macro level discourse structure analysis, which has been less studied in the previous researches. We explore a macro discourse structure presentation schema to present the macro level discourse structure, and propose a corresponding corpus, named Macro Chinese Discourse Treebank. On these bases, we concentrate on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. In order to reduce the error transmission between the associated tasks, we adopt a joint model of the two tasks, and an Integer Linear Programming approach is proposed to achieve global optimization with various kinds of constraints.
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
Authors: Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Longbiao Wang, Xiangang Li
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Relation, Sparse Learning, Text Summarization

Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separately, the ones considering pair specific clues ignore the bidirectional interactions between two arguments and the sparsity of pair patterns. In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition. (1) We mine the most correlated word pairs from two discourse arguments to model pair specific clues, and integrate them as interactive attention into argument representations produced by the bidirectional long short-term memory network. Meanwhile, (2) the neural tensor network with sparse constraint is proposed to explore the deeper and the more important pair patterns so as to fully recognize discourse relations. The experimental results on PDTB show that our proposed TIASL framework is effective.
Transition-based Neural RST Parsing with Implicit Syntax Features
Authors: Nan Yu, Meishan Zhang, Guohong Fu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Discourse Parsing, Word Embeddings

Syntax has been a useful source of information for statistical RST discourse parsing. Under the neural setting, a common approach integrates syntax by a recursive neural network (RNN), requiring discrete output trees produced by a supervised syntax parser. In this paper, we propose an implicit syntax feature extraction approach, using hidden-layer vectors extracted from a neural syntax parser. In addition, we propose a simple transition-based model as the baseline, further enhancing it with dynamic oracle. Experiments on the standard dataset show that our baseline model with dynamic oracle is highly competitive. When implicit syntax features are integrated, we are able to obtain further improvements, better than using explicit Tree-RNN.
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification
Authors: Yudai Kishimoto, Yugo Murawaki, Sadao Kurohashi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Implicit Discourse Relation Classification, Machine Translation, Relation, Relation Classification, Sentiment Analysis, World Knowledge

Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. Contextual information provided no significant gain for implicit discourse relations, but it did for explicit ones.
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features
Authors: Makoto Morishita, Jun Suzuki, Masaaki Nagata
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, NMT, Translation

This paper focuses on subword-based Neural Machine Translation (NMT). We hypothesize that in the NMT model, the appropriate subword units for the following three modules (layers) can differ: (1) the encoder embedding layer, (2) the decoder embedding layer, and (3) the decoder output layer. We find the subword based on Sennrich et al. (2016) has a feature that a large vocabulary is a superset of a small vocabulary and modify the NMT model enables the incorporation of several different subword units in a single embedding layer. We refer these small subword features as hierarchical subword features. To empirically investigate our assumption, we compare the performance of several different subword units and hierarchical subword features for both the encoder and decoder embedding layers. We confirmed that incorporating hierarchical subword features in the encoder consistently improves BLEU scores on the IWSLT evaluation datasets.
Systematic Study of Long Tail Phenomena in Entity Linking
Authors: Filip Ilievski, Piek Vossen, Stefan Schlobach
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Linking

State-of-the-art entity linkers achieve high accuracy scores with probabilistic methods. However, these scores should be considered in relation to the properties of the datasets they are evaluated on. Until now, there has not been a systematic investigation of the properties of entity linking datasets and their impact on system performance. In this paper we report on a series of hypotheses regarding the long tail phenomena in entity linking datasets, their interaction, and their impact on system performance. Our systematic study of these hypotheses shows that evaluation datasets mainly capture head entities and only incidentally cover data from the tail, thus encouraging systems to overfit to popular/frequent and non-ambiguous cases. We find the most difficult cases of entity linking among the infrequent candidates of ambiguous forms. With our findings, we hope to inspire future designs of both entity linking systems and evaluation datasets. To support this goal, we provide a list of recommended actions for better inclusion of tail cases.
Exploiting Structure in Representation of Named Entities using Active Learning
Authors: Nikita Bhutani, Kun Qian, Yunyao Li, H. V. Jagadish, Hern, Mauricio ez, Mitesh Vasa
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Active Learning, Entity Linking, Entity Resolution, Question Answering, Relation Extraction, Text Summarization

Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.
An Empirical Study on Fine-Grained Named Entity Recognition
Authors: Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Chatbot, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Open-Ended Question Answering

Named entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages. It is still an open question whether there is a model that is robust across various settings or the proper model varies depending on the language, the number of named entity categories, and the size of training datasets. This paper first presents an empirical comparison of FG-NER models for English and Japanese and demonstrates that LSTM+CNN+CRF (Ma and Hovy, 2016), one of the state-of-the-art methods for English NER, also works well for English FG-NER but does not work well for Japanese, a language that has a large number of character types. To tackle this problem, we propose a method to improve the neural network-based Japanese FG-NER performance by removing the CNN layer and utilizing dictionary and category embeddings. Experiment results show that the proposed method improves Japanese FG-NER F-score from 66.76{\%} to 75.18{\%}.