pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Reactions and Recommending Products with Generative Models of Reviews
Authors: Jianmo Ni, Zachary C. Lipton, Sharad Vikram, Julian McAuley
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Collaborative Filtering, Language Modeling, Language Modelling, Recommendation Systems, Sentiment Analysis

Traditional approaches to recommendation focus on learning from large volumes of historical feedback to estimate simple numerical quantities (Will a user click on a product? Make a purchase? etc.). Natural language approaches that model information like product reviews have proved to be incredibly useful in improving the performance of such methods, as reviews provide valuable auxiliary information that can be used to better estimate latent user preferences and item properties. In this paper, rather than using reviews as an inputs to a recommender system, we focus on generating reviews as the model{'}s output. This requires us to efficiently model text (at the character level) to capture the preferences of the user, the properties of the item being consumed, and the interaction between them (i.e., the user{'}s preference). We show that this can model can be used to (a) generate plausible reviews and estimate nuanced reactions; (b) provide personalized rankings of existing reviews; and (c) recommend existing products more effectively.
Summarizing Lengthy Questions
Authors: Tatsuya Ishigaki, Hiroya Takamura, Manabu Okumura
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Abstractive Text Summarization, Community Question Answering, Decoder, Question Answering

In this research, we propose the task of question summarization. We first analyzed question-summary pairs extracted from a Community Question Answering (CQA) site, and found that a proportion of questions cannot be summarized by extractive approaches but requires abstractive approaches. We created a dataset by regarding the question-title pairs posted on the CQA site as question-summary pairs. By using the data, we trained extractive and abstractive summarization models, and compared them based on ROUGE scores and manual evaluations. Our experimental results show an abstractive method using an encoder-decoder model with a copying mechanism achieves better scores for both ROUGE-2 F-measure and the evaluations by human judges.
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization
Authors: Tobias Falke, Christian M. Meyer, Iryna Gurevych
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: coreference-resolution, Coreference Resolution, Document Summarization, Multi-Document Summarization

Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps. In this work, we propose a new model for the task that addresses several issues in previous methods. It learns to identify and merge coreferent concepts to reduce redundancy, determines their importance with a strong supervised model and finds an optimal summary concept map via integer linear programming. It is also computationally more efficient than previous methods, allowing us to summarize larger document sets. We evaluate the model on two datasets, finding that it outperforms several approaches from previous work.
Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization
Authors: Litton J Kurisinkel, Yue Zhang, Vasudeva Varma
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization

Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization. The method entrusts the summarizer to generate its own topically coherent sequential structures from scratch for effective communication. Results on TAC 2011, DUC-2004 and 2005 show that our system gives competitive results compared with state of the art abstractive summarization approaches in the literature. We also achieve competitive results in linguistic quality assessed by human evaluators.
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs
Authors: Alex Judea, Michael Strube
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Feature Engineering

In this paper we investigate the performance of event argument identification. We show that the performance is tied to syntactic complexity. Based on this finding, we propose a novel and effective system for event argument identification. Recurrent Neural Networks learn to produce meaningful representations of long and short dependency paths. Convolutional Neural Networks learn to decompose the lexical context of argument candidates. They are combined into a simple system which outperforms a feature-based, state-of-the-art event argument identifier without any manual feature engineering.
Selective Decoding for Cross-lingual Open Information Extraction
Authors: Sheng Zhang, Kevin Duh, Benjamin Van Durme
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Machine Translation, Open Information Extraction

Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.
Event Ordering with a Generalized Model for Sieve Prediction Ranking
Authors: Bill McDowell, Nathanael Chambers, Alex Ororbia II, er, David Reitter
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Reranking, Word Embeddings

This paper improves on several aspects of a sieve-based event ordering architecture, CAEVO (Chambers et al., 2014), which creates globally consistent temporal relations between events and time expressions. First, we examine the usage of word embeddings and semantic role features. With the incorporation of these new features, we demonstrate a 5{\%} relative F1 gain over our replicated version of CAEVO. Second, we reformulate the architecture{'}s sieve-based inference algorithm as a prediction reranking method that approximately optimizes a scoring function computed using classifier precisions. Within this prediction reranking framework, we propose an alternative scoring function, showing an 8.8{\%} relative gain over the original CAEVO. We further include an in-depth analysis of one of the main datasets that is used to evaluate temporal classifiers, and we show how despite using the densest corpus, there is still a danger of overfitting. While this paper focuses on temporal ordering, its results are applicable to other areas that use sieve-based architectures.
Open Relation Extraction and Grounding
Authors: Dian Yu, Lifu Huang, Heng Ji
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Relation, Relation Extraction, Sentence, slot-filling, Slot Filling

Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora. However, they lack of abilities to cover diverse relation expressions or measure the relative importance of candidate triples within a sentence. It is also challenging to name the relation type of a relational triple merely based on context words, which could limit the usefulness of open RE in downstream applications. We propose a novel importance-based open RE approach by exploiting the global structure of a dependency tree to extract salient triples. We design an unsupervised relation type naming method by grounding relational triples to a large-scale Knowledge Base (KB) schema, leveraging KB triples and weighted context words associated with relational triples. Experiments on the English Slot Filling 2013 dataset demonstrate that our approach achieves 8.1{\%} higher F-score over state-of-the-art open RE methods.
Extraction of Gene-Environment Interaction from the Biomedical Literature
Authors: Jinseon You, Jin-Woo Chung, Wonsuk Yang, Jong C. Park
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Named Entity Recognition (NER)

Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.
Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation
Authors: Liangming Pan, Xiaochen Wang, Chengjiang Li, Juanzi Li, Jie Tang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11

Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. One challenging issue in MOOCs is how to design effective and fine-grained course concepts such that students with different backgrounds can grasp the essence of the course. In this paper, we conduct a systematic investigation of the problem of course concept extraction for MOOCs. We propose to learn latent representations for candidate concepts via an embedding-based method. Moreover, we develop a graph-based propagation algorithm to rank the candidate concepts based on the learned representations. We evaluate the proposed method using different courses from XuetangX and Coursera. Experimental results show that our method significantly outperforms all the alternative methods (+0.013-0.318 in terms of R-precision; p{\textless}{\textless}0.01, t-test).
Identity Deception Detection
Authors: Ver{\'o}nica P{\'e}rez-Rosas, Quincy Davenport, Anna Mengdan Dai, Mohamed Abouelenien, Rada Mihalcea
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Deception Detection

This paper addresses the task of detecting identity deception in language. Using a novel identity deception dataset, consisting of real and portrayed identities from 600 individuals, we show that we can build accurate identity detectors targeting both age and gender, with accuracies of up to 88. We also perform an analysis of the linguistic patterns used in identity deception, which lead to interesting insights into identity portrayers.
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Authors: Yuan Ling, Sadid A. Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, Oladimeji Farri
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence

Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating clinical diagnosis as a reinforcement learning problem. During training, the reinforcement learning agent mimics the clinician{'}s cognitive process and learns the optimal policy to obtain the most appropriate diagnoses for a clinical narrative. This is achieved through an iterative search for candidate diagnoses from external knowledge sources via a sentence-by-sentence analysis of the inherent clinical context. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses. Experiments on the TREC CDS datasets demonstrate the effectiveness of our system over various non-reinforcement learning-based systems.
Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems
Authors: Louisa Pragst, Koichiro Yoshino, Wolfgang Minker, Satoshi Nakamura, Stefan Ultes
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Cultural Vocal Bursts Intensity Prediction, Spoken Dialogue Systems

In a dialogue system, the dialogue manager selects one of several system actions and thereby determines the system{'}s behaviour. Defining all possible system actions in a dialogue system by hand is a tedious work. While efforts have been made to automatically generate such system actions, those approaches are mostly focused on providing functional system behaviour. Adapting the system behaviour to the user becomes a difficult task due to the limited amount of system actions available. We aim to increase the adaptability of a dialogue system by automatically generating variants of system actions. In this work, we introduce an approach to automatically generate action variants for elaborateness and indirectness. Our proposed algorithm extracts RDF triplets from a knowledge base and rates their relevance to the original system action to find suitable content. We show that the results of our algorithm are mostly perceived similarly to human generated elaborateness and indirectness and can be used to adapt a conversation to the current user and situation. We also discuss where the results of our algorithm are still lacking and how this could be improved: Taking into account the conversation topic as well as the culture of the user is likely to have beneficial effect on the user{'}s perception.
Demographic Word Embeddings for Racism Detection on Twitter
Authors: Mohammed Hasanuzzaman, Ga{\"e}l Dias, Andy Way
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, General Classification, Word Embeddings

Most social media platforms grant users freedom of speech by allowing them to freely express their thoughts, beliefs, and opinions. Although this represents incredible and unique communication opportunities, it also presents important challenges. Online racism is such an example. In this study, we present a supervised learning strategy to detect racist language on Twitter based on word embedding that incorporate demographic (Age, Gender, and Location) information. Our methodology achieves reasonable classification accuracy over a gold standard dataset (F1=76.3{\%}) and significantly improves over the classification performance of demographic-agnostic models.
Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Authors: Itsumi Saito, Kyosuke Nishida, Kugatsu Sadamitsu, Kuniko Saito, Junji Tomita
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, Morphological Analysis, Text Normalization

Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalization pairs into Japanese morphological analysis. The experimental results show that our method can extract widely covered variants from large Twitter data and improve the recall of normalization without degrading the overall accuracy of Japanese morphological analysis.
Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering
Authors: Mio Kobayashi, Ai Ishii, Chikara Hoshino, Hiroshi Miyashita, Takuya Matsuzaki
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Fact Checking, Passage Retrieval, Question Answering, Retrieval

This paper presents a hybrid approach to the verification of statements about historical facts. The test data was collected from the world history examinations in a standardized achievement test for high school students. The data includes various kinds of false statements that were carefully written so as to deceive the students while they can be disproven on the basis of the teaching materials. Our system predicts the truth or falsehood of a statement based on text search, word cooccurrence statistics, factoid-style question answering, and temporal relation recognition. These features contribute to the judgement complementarily and achieved the state-of-the-art accuracy.
Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base
Authors: Wei-Chuan Hsiao, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Question Answering, Relation

This paper presents an approach to identify subject, type and property from knowledge base (KB) for answering simple questions. We propose new features to rank entity candidates in KB. Besides, we split a relation in KB into type and property. Each of them is modeled by a bi-directional LSTM. Experimental results show that our model achieves the state-of-the-art performance on the SimpleQuestions dataset. The hard questions in the experiments are also analyzed in detail.
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework
Authors: Aaron Steven White, Pushpendre Rastogi, Kevin Duh, Benjamin Van Durme
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: General Classification, Image Captioning, Natural Language Inference, RTE, Semantic Role Labeling, Sentence

We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model{'}s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.
Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model
Authors: Eva D{'}hondt, Cyril Grouin, Brigitte Grau
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Language Modeling, Language Modelling, Named Entity Recognition (NER), Optical Character Recognition (OCR)

In this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or external information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of (relatively) clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short-Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, including a real-life OCR corpus in the medical domain.
Roles and Success in Wikipedia Talk Pages: Identifying Latent Patterns of Behavior
Authors: Keith Maki, Michael Yoder, Yohan Jo, Carolyn Ros{\'e}
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11

In this work we investigate how role-based behavior profiles of a Wikipedia editor, considered against the backdrop of roles taken up by other editors in discussions, predict the success of the editor at achieving an impact on the associated article. We first contribute a new public dataset including a task predicting the success of Wikipedia editors involved in discussion, measured by an operationalization of the lasting impact of their edits in the article. We then propose a probabilistic graphical model that advances earlier work inducing latent discussion roles using the light supervision of success in the negotiation task. We evaluate the performance of the model and interpret findings of roles and group configurations that lead to certain outcomes on Wikipedia.
CKY-based Convolutional Attention for Neural Machine Translation
Authors: Taiki Watanabe, Akihiro Tamura, Takashi Ninomiya
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Sentence, Translation

This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm. The proposed attention represents every possible combination of source words (e.g., phrases and structures) through CNNs, which imitates the CKY table in the algorithm. NMT, incorporating the proposed attention, decodes a target sentence on the basis of the attention scores of the hidden states of CNNs. The proposed attention enables NMT to capture alignments from underlying structures of a source sentence without sentence parsing. The evaluations on the Asian Scientific Paper Excerpt Corpus (ASPEC) English-Japanese translation task show that the proposed attention gains 0.66 points in BLEU.
Supervised Attention for Sequence-to-Sequence Constituency Parsing
Authors: Hidetaka Kamigaito, Katsuhiko Hayashi, Tsutomu Hirao, Hiroya Takamura, Manabu Okumura, Masaaki Nagata
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Constituency Parsing, Machine Translation, Text Generation, Text Summarization, Translation

The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT). Recently, MT performances were improved by incorporating supervised attention into the model. In this paper, we introduce supervised attention to constituency parsing that can be regarded as another translation task. Evaluation results on the PTB corpus showed that the bracketing F-measure was improved by supervised attention.
Neural Lattice Search for Domain Adaptation in Machine Translation
Authors: Huda Khayrallah, Gaurav Kumar, Kevin Duh, Matt Post, Philipp Koehn
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.