pwc-archive/papers-with-abstracts huggingface dataset:
Hyperspherical Query Likelihood Models with Word Embeddings
Authors: Ryo Masumura, Taichi Asami, Hirokazu Masataki, Kugatsu Sadamitsu, Kyosuke Nishida, Ryuichiro Higashinaka
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Information Retrieval, Language Modeling, Language Modelling, Retrieval, Semantic Textual Similarity, Word Embeddings

This paper presents an initial study on hyperspherical query likelihood models (QLMs) for information retrieval (IR). Our motivation is to naturally utilize pre-trained word embeddings for probabilistic IR. To this end, key idea is to directly leverage the word embeddings as random variables for directional probabilistic models based on von Mises-Fisher distributions which are familiar to cosine distances. The proposed method enables us to theoretically take semantic similarities between document and target queries into consideration without introducing heuristic expansion techniques. In addition, this paper reveals relationships between hyperspherical QLMs and conventional QLMs. Experiments show document retrieval evaluation results in which a hyperspherical QLM is compared to conventional QLMs and document distance metrics using word or document embeddings.
Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering
Authors: Kaustubh Kulkarni, Riku Togashi, Hideyuki Maeda, Sumio Fujita
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Question Answering

Embedding based approaches are shown to be effective for solving simple Question Answering (QA) problems in recent works. The major drawback of current approaches is that they look only at the similarity (constraint) between a question and a head, relation pair. Due to the absence of tail (answer) in the questions, these models often require paraphrase datasets to obtain adequate embeddings. In this paper, we propose a dual constraint model which exploits the embeddings obtained by Trans* family of algorithms to solve the simple QA problem without using any additional resources such as paraphrase datasets. The results obtained prove that the embeddings learned using dual constraints are better than those with single constraint models having similar architecture.
Efficiency-aware Answering of Compositional Questions using Answer Type Prediction
Authors: David Ziegler, Abdalghani Abujabal, Rishiraj Saha Roy, Gerhard Weikum
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Question Answering, Type prediction, Vocal Bursts Type Prediction

This paper investigates the problem of answering compositional factoid questions over knowledge bases (KB) under efficiency constraints. The method, called TIPI, (i) decomposes compositional questions, (ii) predicts answer types for individual sub-questions, (iii) reasons over the compatibility of joint types, and finally, (iv) formulates compositional SPARQL queries respecting type constraints. TIPI{'}s answer type predictor is trained using distant supervision, and exploits lexical, syntactic and embedding-based features to compute context- and hierarchy-aware candidate answer types for an input question. Experiments on a recent benchmark show that TIPI results in state-of-the-art performance under the real-world assumption that only a single SPARQL query can be executed over the KB, and substantial reduction in the number of queries in the more general case.
High Recall Open IE for Relation Discovery
Authors: Hady Elsahar, Christophe Gravier, Frederique Laforest
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Open Information Extraction, Relation, Relation Extraction, Sentence, Vocal Bursts Intensity Prediction

Relation Discovery discovers predicates (relation types) from a text corpus relying on the co-occurrence of two named entities in the same sentence. This is a very narrowing constraint: it represents only a small fraction of all relation mentions in practice. In this paper we propose a high recall approach for Open IE, which enables covering up to 16 times more sentences in a large corpus. Comparison against OpenIE systems shows that our proposed approach achieves 28{\%} improvement over the highest recall OpenIE system and 6{\%} improvement in precision than the same system.
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory
Authors: Yu-Lun Hsieh, Yung-Chun Chang, Nai-Wen Chang, Wen-Lian Hsu
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Cross-corpus, Feature Engineering

In this paper, we propose a recurrent neural network model for identifying protein-protein interactions in biomedical literature. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that our approach significantly surpasses state-of-the-art methods with relative improvements of 10{\%} and 18{\%}, respectively. Cross-corpus evaluation also demonstrate that the proposed model remains robust despite using different training data. These results suggest that RNN can effectively capture semantic relationships among proteins as well as generalizes over different corpora, without any feature engineering.
Identifying Empathetic Messages in Online Health Communities
Authors: Hamed Khanpour, Cornelia Caragea, Prakhar Biyani
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11

Empathy captures one{'}s ability to correlate with and understand others{'} emotional states and experiences. Messages with empathetic content are considered as one of the main advantages for joining online health communities due to their potential to improve people{'}s moods. Unfortunately, to this date, no computational studies exist that automatically identify empathetic messages in online health communities. We propose a combination of Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks, and show that the proposed model outperforms each individual model (CNN and LSTM) as well as several baselines.
Fake News Detection Through Multi-Perspective Speaker Profiles
Authors: Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, Chu-Ren Huang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Articles, Fake News Detection

Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5{\%} in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels
Authors: Itsumi Saito, Jun Suzuki, Kyosuke Nishida, Kugatsu Sadamitsu, Satoshi Kobashikawa, Ryo Masumura, Yuji Matsumoto, Junji Tomita
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Data Augmentation, Decoder, Machine Translation, Text Normalization, Text Summarization, Translation

In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models. Attention based encoder-decoder models are greatly effective in generating many natural languages. {\%} such as machine translation or machine summarization. In general, we have to prepare for a large amount of training data to train an encoder-decoder model. Unlike machine translation, there are few training data for text-normalization tasks. In this paper, we propose two methods for generating augmented data. The experimental results with Japanese dialect normalization indicate that our methods are effective for an encoder-decoder model and achieve higher BLEU score than that of baselines. We also investigated the oracle performance and revealed that there is sufficient room for improving an encoder-decoder model.
Using Social Networks to Improve Language Variety Identification with Neural Networks
Authors: Yasuhide Miura, Tomoki Taniguchi, Motoki Taniguchi, Shotaro Misawa, Tomoko Ohkuma
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Language Identification

We propose a hierarchical neural network model for language variety identification that integrates information from a social network. Recently, language variety identification has enjoyed heightened popularity as an advanced task of language identification. The proposed model uses additional texts from a social network to improve language variety identification from two perspectives. First, they are used to introduce the effects of homophily. Secondly, they are used as expanded training data for shared layers of the proposed model. By introducing information from social networks, the model improved its accuracy by 1.67-5.56. Compared to state-of-the-art baselines, these improved performances are better in English and comparable in Spanish. Furthermore, we analyzed the cases of Portuguese and Arabic when the model showed weak performances, and found that the effect of homophily is likely to be weak due to sparsity and noises compared to languages with the strong performances.
Improving Japanese-to-English Neural Machine Translation by Voice Prediction
Authors: Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, Mamoru Komachi
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, Sentence, Translation

This study reports an attempt to predict the voice of reference using the information from the input sentences or previous input/output sentences. Our previous study presented a voice controlling method to generate sentences for neural machine translation, wherein it was demonstrated that the BLEU score improved when the voice of generated sentence was controlled relative to that of the reference. However, it is impractical to use the reference information because we cannot discern the voice of the correct translation in advance. Thus, this study presents a voice prediction method for generated sentences for neural machine translation. While evaluating on Japanese-to-English translation, we obtain a 0.70-improvement in the BLEU using the predicted voice.
Key-value Attention Mechanism for Neural Machine Translation
Authors: Hideya Mino, Masao Utiyama, Eiichiro Sumita, Takenobu Tokunaga
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Translation

In this paper, we propose a neural machine translation (NMT) with a key-value attention mechanism on the source-side encoder. The key-value attention mechanism separates the source-side content vector into two types of memory known as the key and the value. The key is used for calculating the attention distribution, and the value is used for encoding the context representation. Experiments on three different tasks indicate that our model outperforms an NMT model with a conventional attention mechanism. Furthermore, we perform experiments with a conventional NMT framework, in which a part of the initial value of a weight matrix is set to zero so that the matrix is as the same initial-state as the key-value attention mechanism. As a result, we obtain comparable results with the key-value attention mechanism without changing the network structure.
Concept Equalization to Guide Correct Training of Neural Machine Translation
Authors: Kangil Kim, Jong-Hun Shin, Seung-Hoon Na, SangKeun Jung
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Translation

Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences. This approach is limited to find the best word composition and requires help of explicit methods as beam search. To help learning correct compositional mechanisms in NMTs, we propose concept equalization using direct mapping distributed representations of source and target sentences. In a translation experiment from English to French, the concept equalization significantly improved translation quality by 3.00 BLEU points compared to a state-of-the-art NMT model.
Building Large Chinese Corpus for Spoken Dialogue Research in Specific Domains
Authors: Changliang Li, Xiuying Wang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Information Retrieval, Retrieval, Sentence

Corpus is a valuable resource for information retrieval and data-driven natural language processing systems,especially for spoken dialogue research in specific domains. However,there is little non-English corpora, particular for ones in Chinese. Spoken by the nation with the largest population in the world, Chinese become increasingly prevalent and popular among millions of people worldwide. In this paper, we build a large-scale and high-quality Chinese corpus, called CSDC (Chinese Spoken Dialogue Corpus). It contains five domains and more than 140 thousand dialogues in all. Each sentence in this corpus is annotated with slot information additionally compared to other corpora. To our best knowledge, this is the largest Chinese spoken dialogue corpus, as well as the first one with slot information. With this corpus, we proposed a method and did a well-designed experiment. The indicative result is reported at last.
Identifying Speakers and Listeners of Quoted Speech in Literary Works
Authors: Chak Yan Yeung, John Lee
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Segmentation, Speaker Identification

We present the first study that evaluates both speaker and listener identification for direct speech in literary texts. Our approach consists of two steps: identification of speakers and listeners near the quotes, and dialogue chain segmentation. Evaluation results show that this approach outperforms a rule-based approach that is state-of-the-art on a corpus of literary texts.
Language-Independent Prediction of Psycholinguistic Properties of Words
Authors: Yo Ehara
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Lexical Simplification, Prediction

The psycholinguistic properties of words, namely, word familiarity, age of acquisition, concreteness, and imagery, have been reported to be effective for educational natural language-processing tasks. Previous studies on predicting the values of these properties rely on language-dependent features. This paper is the first to propose a practical language-independent method for predicting such values by using only a large raw corpus in a language. Through experiments, our method successfully predicted the values of these properties in two languages. The results for English were competitive with the reported accuracy achieved using features specific to English.
Correlation Analysis of Chronic Obstructive Pulmonary Disease (COPD) and its Biomarkers Using the Word Embeddings
Authors: Byeong-Hun Yoon, Yu-Seop Kim
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Word Embeddings

It is very costly and time consuming to find new biomarkers for specific diseases in clinical laboratories. In this study, to find new biomarkers most closely related to Chronic Obstructive Pulmonary Disease (COPD), which is widely known as respiratory disease, biomarkers known to be associated with respiratory diseases and COPD itself were converted into word embedding. And their similarities were measured. We used Word2Vec, Canonical Correlation Analysis (CCA), and Global Vector (GloVe) for word embedding. In order to replace the clinical evaluation, the titles and abstracts of papers retrieved from Google Scholars were analyzed and quantified to estimate the performance of the word em-bedding models.
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems
Authors: Hiroki Asano, Tomoya Mizumoto, Kentaro Inui
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Grammatical Error Correction, Machine Translation

In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage. To address this problem, a reference-less approach has recently emerged; however, previous reference-less metrics that only consider the criterion of grammaticality, have not worked as well as reference-based metrics. This study explores the potential of extending a prior grammaticality-based method to establish a reference-less evaluation method for GEC systems. Further, we empirically show that a reference-less metric that combines fluency and meaning preservation with grammaticality provides a better estimate of manual scores than that of commonly used reference-based metrics. To our knowledge, this is the first study that provides empirical evidence that a reference-less metric can replace reference-based metrics in evaluating GEC systems.
CVBed: Structuring CVs usingWord Embeddings
Authors: Shweta Garg, Sudhanshu S Singh, Abhijit Mishra, Kuntal Dey
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Word Embeddings

Automatic analysis of curriculum vitae (CVs) of applicants is of tremendous importance in recruitment scenarios. The semi-structuredness of CVs, however, makes CV processing a challenging task. We propose a solution towards transforming CVs to follow a unified structure, thereby, paving ways for smoother CV analysis. The problem of restructuring is posed as a section relabeling problem, where each section of a given CV gets reassigned to a predefined label. Our relabeling method relies on semantic relatedness computed between section header, content and labels, based on phrase-embeddings learned from a large pool of CVs. We follow different heuristics to measure semantic relatedness. Our best heuristic achieves an F-score of 93.17{\%} on a test dataset with gold-standard labels obtained using manual annotation.
Leveraging Diverse Lexical Chains to Construct Essays for Chinese College Entrance Examination
Authors: Liunian Li, Xiaojun Wan, Jin-Ge Yao, Siming Yan
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Sentence

In this work we study the challenging task of automatically constructing essays for Chinese college entrance examination where the topic is specified in advance. We explore a sentence extraction framework based on diversified lexical chains to capture coherence and richness. Experimental analysis shows the effectiveness of our approach and reveals the importance of information richness in essay writing.
Draw and Tell: Multimodal Descriptions Outperform Verbal- or Sketch-Only Descriptions in an Image Retrieval Task
Authors: Ting Han, David Schlangen
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Image Description, Image Retrieval, Retrieval

While language conveys meaning largely symbolically, actual communication acts typically contain iconic elements as well: People gesture while they speak, or may even draw sketches while explaining something. Image retrieval prima facie seems like a task that could profit from combined symbolic and iconic reference, but it is typically set up to work either from language only, or via (iconic) sketches with no verbal contribution. Using a model of grounded language semantics and a model of sketch-to-image mapping, we show that adding even very reduced iconic information to a verbal image description improves recall. Verbal descriptions paired with fully detailed sketches still perform better than these sketches alone. We see these results as supporting the assumption that natural user interfaces should respond to multimodal input, where possible, rather than just language alone.
Coreference Resolution on Math Problem Text in Japanese
Authors: Takumi Ito, Takuya Matsuzaki, Satoshi Sato
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: coreference-resolution, Coreference Resolution, Math

This paper describes a coreference resolution system for math problem text. Case frame dictionaries and a math taxonomy are utilized for supplying domain knowledge. The system deals with various anaphoric phenomena beyond well-studied entity coreferences.
Utilizing Visual Forms of Japanese Characters for Neural Review Classification
Authors: Yota Toyama, Makoto Miwa, Yutaka Sasaki
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, General Classification, Sentence

We propose a novel method that exploits visual information of ideograms and logograms in analyzing Japanese review documents. Our method first converts font images of Japanese characters into character embeddings using convolutional neural networks. It then constructs document embeddings from the character embeddings based on Hierarchical Attention Networks, which represent the documents based on attention mechanisms from a character level to a sentence level. The document embeddings are finally used to predict the labels of documents. Our method provides a way to exploit visual features of characters in languages with ideograms and logograms. In the experiments, our method achieved an accuracy comparable to a character embedding-based model while our method has much fewer parameters since it does not need to keep embeddings of thousands of characters.
A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition
Authors: Dingquan Wang, Nanyun Peng, Kevin Duh
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Cross-Lingual Transfer, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

We show how to adapt bilingual word embeddings (BWE{'}s) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task.
Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems
Authors: Koh Mitsuda, Ryuichiro Higashinaka, Junji Tomita
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11

In dialogue systems, conveying understanding results of user utterances is important because it enables users to feel understood by the system. However, it is not clear what types of understanding results should be conveyed to users; some utterances may be offensive and some may be too commonsensical. In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects. As a result, we found that only certain types of understanding results, such as those related to a user{'}s permanent state, are effective to improve user satisfaction. This paper clarifies the types of understanding results that can be safely uttered by a system.
Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences
Authors: Ebuka Ibeke, Chenghua Lin, Adam Wyner, Mohamad Hardyman Barawi
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Opinion Mining

Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts. Most existing studies separate input data into respective collections. In addition, the relationships between the topics extracted and the sentences in the corpus which express the topics are opaque, hindering our understanding of the opinions expressed in the corpus. We propose a novel unified latent variable model (contraLDA) which addresses the above matters. Experimental results show the effectiveness of our model in mining contrasted opinions, outperforming our baselines.