pwc-archive/papers-with-abstracts huggingface dataset:
Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF
Authors: Yang Xiang, Xiaoqiang Zhou, Qingcai Chen, Zhihui Zheng, Buzhou Tang, Xiaolong Wang, Yang Qin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Community Question Answering, Question Answering

In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we propose two neural network-based models, with different combination modes of Convolutional Neural Net-works, Long Short Term Memory and Conditional Random Fields. Extensive experi-ments are taken on the dataset released by the SemEval-2015 cQA shared task. The first model is a stacked ensemble of the networks. It achieves 58.96{\%} on macro averaged F1, which improves the state-of-the-art neural network-based method by 2.82{\%} and outper-forms the Top-1 system in the shared task by 1.77{\%}. The second is a simple attention-based model whose input is the connection of the question and its corresponding answers. It produces promising results with 58.29{\%} on overall F1 and gains the best performance on the Good and Bad categories.
Semantically Motivated Hebrew Verb-Noun Multi-Word Expressions Identification
Authors: Chaya Liebeskind, Yaakov HaCohen-Kerner
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Information Retrieval, Machine Translation, Question Answering, Text Generation, Text Summarization, Word Sense Disambiguation

Identification of Multi-Word Expressions (MWEs) lies at the heart of many natural language processing applications. In this research, we deal with a particular type of Hebrew MWEs, Verb-Noun MWEs (VN-MWEs), which combine a verb and a noun with or without other words. Most prior work on MWEs classification focused on linguistic and statistical information. In this paper, we claim that it is essential to utilize semantic information. To this end, we propose a semantically motivated indicator for classifying VN-MWE and define features that are related to various semantic spaces and combine them as features in a supervised classification framework. We empirically demonstrate that our semantic feature set yields better performance than the common linguistic and statistical feature sets and that combining semantic features contributes to the VN-MWEs identification task.
Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention
Authors: Minguang Xiao, Cong Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification, Relation, Relation Classification, Semantic Composition, Sentence

Semantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for relation classification. Our model has several distinctive features: (1) Each sentence is divided into three context subsequences according to two annotated nominals, which allows the model to encode each context subsequence independently so as to selectively focus as on the important context information; (2) The hierarchical model consists of two recurrent neural networks (RNNs): the first one learns context representations of the three context subsequences respectively, and the second one computes semantic composition of these three representations and produces a sentence representation for the relationship classification of the two nominals. (3) The attention mechanism is adopted in both RNNs to encourage the model to concentrate on the important information when learning the sentence representations. Experimental results on the SemEval-2010 Task 8 dataset demonstrate that our model is comparable to the state-of-the-art without using any hand-crafted features.
A Unified Architecture for Semantic Role Labeling and Relation Classification
Authors: Jiang Guo, Wanxiang Che, Haifeng Wang, Ting Liu, Jun Xu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, Feature Engineering, General Classification, Information Retrieval, Multi-Task Learning, Named Entity Recognition (NER), Part-Of-Speech Tagging, Relation, Relation Classification, Semantic Role Labeling, Sentence

This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly studied separately in prior work, we show that the two tasks can be effectively connected and modeled using a general architecture. Experiments on CoNLL-2009 benchmark datasets show that our SRL models significantly outperform state-of-the-art approaches. Our RC models also yield competitive performance with the best published records. Furthermore, we show that the two tasks can be trained jointly with multi-task learning, resulting in additive significant improvements for SRL.
Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training
Authors: Quynh Ngoc Thi Do, Steven Bethard, Marie-Francine Moens
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Semantic Role Labeling, Word Embeddings

We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.
Predictability of Distributional Semantics in Derivational Word Formation
Authors: Sebastian Pad{\'o}, Aur{\'e}lie Herbelot, Max Kisselew, Jan {\v{S}}najder
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, regression, Sentiment Analysis, Word Embeddings

Compositional distributional semantic models (CDSMs) have successfully been applied to the task of predicting the meaning of a range of linguistic constructions. Their performance on semi-compositional word formation process of (morphological) derivation, however, has been extremely variable, with no large-scale empirical investigation to date. This paper fills that gap, performing an analysis of CDSM predictions on a large dataset (over 30,000 German derivationally related word pairs). We use linear regression models to analyze CDSM performance and obtain insights into the linguistic factors that influence how predictable the distributional context of a derived word is going to be. We identify various such factors, notably part of speech, argument structure, and semantic regularity.
Linguistic features for Hindi light verb construction identification
Authors: Ashwini Vaidya, Sumeet Agarwal, Martha Palmer
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Diversity, Machine Translation, Translation

Light verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as nirnay lenaa {`}decision take; decide{'} from an ordinary verb-argument combination kaagaz lenaa {`}paper take; take (a) paper{'},it has been shown to aid NLP applications such as parsing (Begum et al., 2011) and machine translation (Pal et al., 2011). In this paper, we propose an LVC identification system using language specific features for Hindi which shows an improvement over previous work(Begum et al., 2011). To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus. We find that our model performs robustly across a diverse range of LVCs and our results underscore the importance of semantic features, which is in keeping with the findings for English. Our error analysis also demonstrates that our classifier can be used to further refine LVC annotations in the Hindi Treebank and make them more consistent across the board.
Cross-lingual Transfer of Correlations between Parts of Speech and Gaze Features
Authors: Maria Barrett, Frank Keller, Anders S{\o}gaard
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Cross-Lingual Transfer, POS, POS Tagging, Sentence Compression

Several recent studies have shown that eye movements during reading provide information about grammatical and syntactic processing, which can assist the induction of NLP models. All these studies have been limited to English, however. This study shows that gaze and part of speech (PoS) correlations largely transfer across English and French. This means that we can replicate previous studies on gaze-based PoS tagging for French, but also that we can use English gaze data to assist the induction of French NLP models.
Chinese Hypernym-Hyponym Extraction from User Generated Categories
Authors: Chengyu Wang, Xiaofeng He
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Knowledge Graphs, Machine Translation, Named Entity Recognition (NER), Reading Comprehension, Relation, Relation Extraction, Word Embeddings

Hypernym-hyponym ({``}is-a{''}) relations are key components in taxonomies, object hierarchies and knowledge graphs. While there is abundant research on is-a relation extraction in English, it still remains a challenge to identify such relations from Chinese knowledge sources accurately due to the flexibility of language expression. In this paper, we introduce a weakly supervised framework to extract Chinese is-a relations from user generated categories. It employs piecewise linear projection models trained on a Chinese taxonomy and an iterative learning algorithm to update models incrementally. A pattern-based relation selection method is proposed to prevent {``}semantic drift{''} in the learning process using bi-criteria optimization. Experimental results illustrate that the proposed approach outperforms state-of-the-art methods.
Dynamic Generative model for Diachronic Sense Emergence Detection
Authors: Martin Emms, Arun Kumar Jayapal
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: model

As time passes words can acquire meanings they did not previously have, such as the {`}twitter post{'} usage of {`}tweet{'}. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the {`}pseudo-word{'} technique (Schutze, 1992) to give a novel further evaluation via {`}pseudo-neologisms{'}. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.
Fast Gated Neural Domain Adaptation: Language Model as a Case Study
Authors: Jian Zhang, Xiaofeng Wu, Andy Way, Qun Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Machine Translation, Translation

Neural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a novel domain adaptation mechanism in neural network training. Instead of learning and adapting the neural network on millions of training sentences {--} which can be very time-consuming or even infeasible in some cases {--} we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead. In our experiments, we use the recurrent neural network language model (LM) as a case study. We show that the neural LM perplexity can be reduced by 7.395 and 12.011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively. Furthermore, we show that using the domain-adapted neural LM to re-rank the statistical machine translation n-best list on the French-to-English language pair can significantly improve translation quality.
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings
Authors: Francisco Guzm{\'a}n, Houda Bouamor, Ramy Baly, Nizar Habash
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Community Question Answering, Machine Translation, Morphological Analysis, Morphological Inflection, Question Answering, Sentence, Sentence Similarity, Translation, Word Embeddings

Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from different levels of lexical and morpho-syntactic linguistic analysis and show that they improve MT evaluation into an MRL. Specifically we report on Arabic, a language with complex and rich morphology. Our results show that using a neural-network model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation into Arabic, by almost over 75{\%} increase in correlation with human judgments on pairwise MT evaluation quality task. More importantly, we demonstrate the usefulness of morpho-syntactic representations to model sentence similarity for MT evaluation and address complex linguistic phenomena of Arabic.
Ensemble Learning for Multi-Source Neural Machine Translation
Authors: Ekaterina Garmash, Christof Monz
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Ensemble Learning, Machine Translation, NMT, Translation

In this paper we describe and evaluate methods to perform ensemble prediction in neural machine translation (NMT). We compare two methods of ensemble set induction: sampling parameter initializations for an NMT system, which is a relatively established method in NMT (Sutskever et al., 2014), and NMT systems translating from different source languages into the same target language, i.e., multi-source ensembles, a method recently introduced by Firat et al. (2016). We are motivated by the observation that for different language pairs systems make different types of mistakes. We propose several methods with different degrees of parameterization to combine individual predictions of NMT systems so that they mutually compensate for each other{'}s mistakes and improve overall performance. We find that the biggest improvements can be obtained from a context-dependent weighting scheme for multi-source ensembles. This result offers stronger support for the linguistic motivation of using multi-source ensembles than previous approaches. Evaluation is carried out for German and French into English translation. The best multi-source ensemble method achieves an improvement of up to 2.2 BLEU points over the strongest single-source ensemble baseline, and a 2 BLEU improvement over a multi-source ensemble baseline.
Phrase-based Machine Translation using Multiple Preordering Candidates
Authors: Yusuke Oda, Taku Kudo, Tetsuji Nakagawa, Taro Watanabe
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder, Machine Translation, Translation

In this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure. Compared with previous phrase-based decoding methods, our method is based on a simple left-to-right dynamic programming in which no decoding-time reordering is performed. As a result, its runtime is very fast and implementing the algorithm becomes easy. Our system does not depend on specific preordering methods as long as they output multiple preordering candidates, and it is trivial to employ existing preordering methods into our system. In our experiments for translating diverse 11 languages into English, the proposed method outperforms conventional phrase-based decoder in terms of translation qualities under comparable or faster decoding time.
Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering
Authors: Sai Praneeth Suggu, Kushwanth Naga Goutham, Manoj K. Chinnakotla, Manish Shrivastava
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Community Question Answering, Question Answering

Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) Deep Learning (DL) techniques which automatically learn the required feature representations. In this paper, we propose a novel approach for AQP known as - {``}Deep Feature Fusion Network (DFFN){''} which combines the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, the DFFN architecture independently - a) learns features from the Deep Neural Network (DNN) and b) computes hand-crafted features using various external resources and then combines them using a fully connected neural network trained to predict the final answer quality. DFFN is end-end differentiable and trained as a single system. We propose two different DFFN architectures which vary mainly in the way they model the input question/answer pair - DFFN-CNN uses a Convolutional Neural Network (CNN) and DFFN-BLNA uses a Bi-directional LSTM with Neural Attention (BLNA). Both these proposed variants of DFFN (DFFN-CNN and DFFN-BLNA) achieve state-of-the-art performance on the standard SemEval-2015 and SemEval-2016 benchmark datasets and outperforms baseline approaches which individually employ either HCF or DL based techniques alone.
Learning Event Expressions via Bilingual Structure Projection
Authors: Fangyuan Li, Ruihong Huang, Deyi Xiong, Min Zhang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Identifying events of a specific type is a challenging task as events in texts are described in numerous and diverse ways. Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts. However, to ensure high quality of learned phrases, this method is constrained to only learn phrases that match certain syntactic structures. In this paper, we propose a bilingual structure projection algorithm that explores linguistic divergences between two languages (Chinese and English) and mines new phrases with new syntactic structures, which have been ignored in the previous work. Experiments show that our approach can successfully find novel event phrases and structures, e.g., phrases headed by nouns. Furthermore, the newly mined phrases are capable of recognizing additional event descriptions and increasing the recall of event recognition.
Global Inference to Chinese Temporal Relation Extraction
Authors: Peifeng Li, Qiaoming Zhu, Guodong Zhou, Hongling Wang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Question Answering, Relation, Relation Extraction, Sentence, Temporal Relation Extraction, Text Generation

Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences. In this paper, we propose a discourse-level global inference model to mine those temporal relations between event mentions in document-level, especially in nonadjacent sentences. Moreover, we provide various kinds of discourse-level constraints, which derived from event semantics, to further improve our global inference model. Evaluation on a Chinese corpus justifies the effectiveness of our discourse-level global inference model over two strong baselines.
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks
Authors: Xiaotian Jiang, Quan Wang, Peng Li, Bin Wang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Multi-Label Learning, Relation, Relation Classification, Relation Extraction, Sentence

Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.
Named Entity Disambiguation for little known referents: a topic-based approach
Authors: Andrea Glaser, Jonas Kuhn
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Entity Disambiguation, Entity Linking, Information Retrieval

We propose an approach to Named Entity Disambiguation that avoids a problem of standard work on the task (likewise affecting fully supervised, weakly supervised, or distantly supervised machine learning techniques): the treatment of name mentions referring to people with no (or very little) coverage in the textual training data is systematically incorrect. We propose to indirectly take into account the property information for the {``}non-prominent{''} name bearers, such as nationality and profession (e.g., for a Canadian law professor named Michael Jackson, with no Wikipedia article, it is very hard to obtain reliable textual training data). The target property information for the entities is directly available from name authority files, or inferrable, e.g., from listings of sportspeople etc. Our proposed approach employs topic modeling to exploit textual training data based on entities sharing the relevant properties. In experiments with a pilot implementation of the general approach, we show that the approach does indeed work well for name/referent pairs with limited textual coverage in the training data.