polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

park-myaeng-2017-computational

A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1022/

Park, Joohee and Myaeng, Sung-hyon

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

214--223

A distributed representation has become a popular approach to capturing a word meaning. Besides its success and practical value, however, questions arise about the relationships between a true word meaning and its distributed representation. In this paper, we examine such a relationship via polymodal embedding approach inspired by the theory that humans tend to use diverse sources in developing a word meaning. The result suggests that the existing embeddings lack in capturing certain aspects of word meanings which can be significantly improved by the polymodal approach. Also, we show distinct characteristics of different types of words (e.g. concreteness) via computational studies. Finally, we show our proposed embedding method outperforms the baselines in the word similarity measure tasks and the hypernym prediction tasks.

null

56,977

inproceedings

konkol-etal-2017-geographical

Geographical Evaluation of Word Embeddings

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1023/

Konkol, Michal and Brychc{\'i}n, Tom{\'a}{\v{s}} and Nykl, Michal and Hercig, Tom{\'a}{\v{s}}

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

224--232

Word embeddings are commonly compared either with human-annotated word similarities or through improvements in natural language processing tasks. We propose a novel principle which compares the information from word embeddings with reality. We implement this principle by comparing the information in the word embeddings with geographical positions of cities. Our evaluation linearly transforms the semantic space to optimally fit the real positions of cities and measures the deviation between the position given by word embeddings and the real position. A set of well-known word embeddings with state-of-the-art results were evaluated. We also introduce a visualization that helps with error analysis.

null

56,978

inproceedings

cao-etal-2017-modeling

On Modeling Sense Relatedness in Multi-prototype Word Embedding

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1024/

Cao, Yixin and Shi, Jiaxin and Li, Juanzi and Liu, Zhiyuan and Li, Chengjiang

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

233--242

To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model. However, most related work ignores the relatedness among word senses which actually plays an important role. In this paper, we propose a novel approach to capture word sense relatedness in multi-prototype word embedding model. Particularly, we differentiate the original sense and extended senses of a word by introducing their global occurrence information and model their relatedness through the local textual context information. Based on the idea of fuzzy clustering, we introduce a random process to integrate these two types of senses and design two non-parametric methods for word sense induction. To make our model more scalable and efficient, we use an online joint learning framework extended from the Skip-gram model. The experimental results demonstrate that our model outperforms both conventional single-prototype embedding models and other multi-prototype embedding models, and achieves more stable performance when trained on smaller data.

null

56,979

inproceedings

takeda-komatani-2017-unsupervised

Unsupervised Segmentation of Phoneme Sequences based on {P}itman-{Y}or Semi-{M}arkov Model using Phoneme Length Context

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1025/

Takeda, Ryu and Komatani, Kazunori

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

243--252

Unsupervised segmentation of phoneme sequences is an essential process to obtain unknown words during spoken dialogues. In this segmentation, an input phoneme sequence without delimiters is converted into segmented sub-sequences corresponding to words. The Pitman-Yor semi-Markov model (PYSMM) is promising for this problem, but its performance degrades when it is applied to phoneme-level word segmentation. This is because of insufficient cues for the segmentation, e.g., homophones are improperly treated as single entries and their different contexts are also confused. We propose a phoneme-length context model for PYSMM to give a helpful cue at the phoneme-level and to predict succeeding segments more accurately. Our experiments showed that the peak performance with our context model outperformed those without such a context model by 0.045 at most in terms of F-measures of estimated segmentation.

null

56,980

inproceedings

zhang-wallace-2017-sensitivity

A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1026/

Zhang, Ye and Wallace, Byron

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

253--263

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et al., 2014; Johnson and Zhang, 2014; Zhang et al., 2016). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.

null

56,981

inproceedings

teranishi-etal-2017-coordination

Coordination Boundary Identification with Similarity and Replaceability

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1027/

Teranishi, Hiroki and Shindo, Hiroyuki and Matsumoto, Yuji

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

264--272

We propose a neural network model for coordination boundary detection. Our method relies on the two common properties - similarity and replaceability in conjuncts - in order to detect both similar pairs of conjuncts and dissimilar pairs of conjuncts. The model improves identification of clause-level coordination using bidirectional RNNs incorporating two properties as features. We show that our model outperforms the existing state-of-the-art methods on the coordination annotated Penn Treebank and Genia corpus without any syntactic information from parsers.

null

56,982

inproceedings

ferret-2017-turning

Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1028/

Ferret, Olivier

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

273--283

In this article, we propose to investigate a new problem consisting in turning a distributional thesaurus into dense word vectors. We propose more precisely a method for performing such task by associating graph embedding and distributed representation adaptation. We have applied and evaluated it for English nouns at a large scale about its ability to retrieve synonyms. In this context, we have also illustrated the interest of the developed method for three different tasks: the improvement of already existing word embeddings, the fusion of heterogeneous representations and the expansion of synsets.

null

56,983

inproceedings

nieto-pina-johansson-2017-training

Training Word Sense Embeddings With Lexicon-based Regularization

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1029/

Nieto-Pi{\~n}a, Luis and Johansson, Richard

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

284--294

We propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm`s objective function through a regularizer. The incorporation of lexicographic data yields embeddings that are able to reflect expert-defined word senses, while retaining the robustness, high quality, and coverage of automatic corpus-based methods. These properties are observed in a manual inspection of the semantic clusters that different degrees of regularizer strength create in the vector space. Moreover, we evaluate the sense embeddings in two downstream applications: word sense disambiguation and semantic frame prediction, where they outperform simpler approaches. Our results show that a corpus-based model balanced with lexicographic data learns better representations and improve their performance in downstream tasks.

null

56,984

inproceedings

alva-manchego-etal-2017-learning

Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1030/

Alva-Manchego, Fernando and Bingel, Joachim and Paetzold, Gustavo and Scarton, Carolina and Specia, Lucia

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

295--305

Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data. While the recently introduced Newsela corpus has alleviated the first problem, simplifications still need to be learned directly from parallel text using black-box, end-to-end approaches rather than from explicit annotations. These complex-simple parallel sentence pairs often differ to such a high degree that generalization becomes difficult. End-to-end models also make it hard to interpret what is actually learned from data. We propose a method that decomposes the task of TS into its sub-problems. We devise a way to automatically identify operations in a parallel corpus and introduce a sequence-labeling approach based on these annotations. Finally, we provide insights on the types of transformations that different approaches can model.

null

56,985

inproceedings

biran-mckeown-2017-domain

Domain-Adaptable Hybrid Generation of {RDF} Entity Descriptions

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1031/

Biran, Or and McKeown, Kathleen

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

306--315

RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity. While they can be useful as a starting point for generating descriptions of entities, they often miss important information about an entity that cannot be captured as simple relations. In addition, generic approaches to generation from RDF cannot capture the unique style and content of specific domains. We describe a framework for hybrid generation of entity descriptions, which combines generation from RDF data with text extracted from a corpus, and extracts unique aspects of the domain from the corpus to create domain-specific generation systems. We show that each component of our approach significantly increases the satisfaction of readers with the text across multiple applications and domains.

null

56,986

inproceedings

pouriyeh-etal-2017-es

{ES}-{LDA}: Entity Summarization using Knowledge-based Topic Modeling

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1032/

Pouriyeh, Seyedamin and Allahyari, Mehdi and Kochut, Krzysztof and Cheng, Gong and Arabnia, Hamid Reza

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

316--325

With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real-world entities, has gained increasing attention in recent years. In this paper, we propose a probabilistic topic model, ES-LDA, that combines prior knowledge with statistical learning techniques within a single framework to create more reliable and representative summaries for entities. We demonstrate the effectiveness of our approach by conducting extensive experiments and show that our model outperforms the state-of-the-art techniques and enhances the quality of the entity summaries.

null

56,987

inproceedings

ushiku-etal-2017-procedural

Procedural Text Generation from an Execution Video

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1033/

Ushiku, Atsushi and Hashimoto, Hayato and Hashimoto, Atsushi and Mori, Shinsuke

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

326--335

In recent years, there has been a surge of interest in automatically describing images or videos in a natural language. These descriptions are useful for image/video search, etc. In this paper, we focus on procedure execution videos, in which a human makes or repairs something and propose a method for generating procedural texts from them. Since video/text pairs available are limited in size, the direct application of end-to-end deep learning is not feasible. Thus we propose to train Faster R-CNN network for object recognition and LSTM for text generation and combine them at run time. We took pairs of recipe and cooking video, generated a recipe from a video, and compared it with the original recipe. The experimental results showed that our method can produce a recipe as accurate as the state-of-the-art scene descriptions.

null

56,988

inproceedings

gan-gong-2017-text

Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1034/

Gan, Ling and Gong, Houyu

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

336--341

Tree-structured Long Short-Term Memory (Tree-LSTM) has been proved to be an effective method in the sentiment analysis task. It extracts structural information on text, and uses Long Short-Term Memory (LSTM) cell to prevent gradient vanish. However, though combining the LSTM cell, it is still a kind of model that extracts the structural information and almost not extracts serialization information. In this paper, we propose three new models in order to combine those two kinds of information: the structural information generated by the Constituency Tree-LSTM and the serialization information generated by Long-Short Term Memory neural network. Our experiments show that combining those two kinds of information can give contributes to the performance of the sentiment analysis task compared with the single Constituency Tree-LSTM model and the LSTM model.

null

56,989

inproceedings

potash-etal-2017-length

Length, Interchangeability, and External Knowledge: Observations from Predicting Argument Convincingness

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1035/

Potash, Peter and Bhattacharya, Robin and Rumshisky, Anna

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

342--351

In this work, we provide insight into three key aspects related to predicting argument convincingness. First, we explicitly display the power that text length possesses for predicting convincingness in an unsupervised setting. Second, we show that a bag-of-words embedding model posts state-of-the-art on a dataset of arguments annotated for convincingness, outperforming an SVM with numerous hand-crafted features as well as recurrent neural network models that attempt to capture semantic composition. Finally, we assess the feasibility of integrating external knowledge when predicting convincingness, as arguments are often more convincing when they contain abundant information and facts. We finish by analyzing the correlations between the various models we propose.

null

56,990

inproceedings

duan-etal-2017-exploiting

Exploiting Document Level Information to Improve Event Detection via Recurrent Neural Networks

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1036/

Duan, Shaoyang and He, Ruifang and Zhao, Wenli

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

352--361

This paper tackles the task of event detection, which involves identifying and categorizing events. The previous work mainly exist two problems: (1) the traditional feature-based methods apply cross-sentence information, yet need taking a large amount of human effort to design complicated feature sets and inference rules; (2) the representation-based methods though overcome the problem of manually extracting features, while just depend on local sentence representation. Considering local sentence context is insufficient to resolve ambiguities in identifying particular event types, therefore, we propose a novel document level Recurrent Neural Networks (DLRNN) model, which can automatically extract cross-sentence clues to improve sentence level event detection without designing complex reasoning rules. Experiment results show that our approach outperforms other state-of-the-art methods on ACE 2005 dataset without external knowledge base.

null

56,991

inproceedings

zhang-etal-2017-embracing

Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1037/

Zhang, Boliang and Lu, Di and Pan, Xiaoman and Lin, Ying and Abudukelimu, Halidanmu and Ji, Heng and Knight, Kevin

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

362--372

Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge. All supervised learning methods (including deep neural networks (DNN)) are sensitive to noise and thus they are not quite portable without massive clean annotations. We found that the F-scores of DNN-based name taggers drop rapidly (20{\%}-30{\%}) when we replace clean manual annotations with noisy annotations in the training data. We propose a new solution to incorporate many non-traditional language universal resources that are readily available but rarely explored in the Natural Language Processing (NLP) community, such as the World Atlas of Linguistic Structure, CIA names, PanLex and survival guides. We acquire and encode various types of non-traditional linguistic resources into a DNN name tagger. Experiments on three low-resource languages show that feeding linguistic knowledge can make DNN significantly more robust to noise, achieving 8{\%}-22{\%} absolute F-score gains on name tagging without using any human annotation

null

56,992

inproceedings

skadina-pinnis-2017-nmt

{NMT} or {SMT}: Case Study of a Narrow-domain {E}nglish-{L}atvian Post-editing Project

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1038/

Skadi{\c{n}}a, Inguna and Pinnis, M{\={a}}rcis

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

373--383

The recent technological shift in machine translation from statistical machine translation (SMT) to neural machine translation (NMT) raises the question of the strengths and weaknesses of NMT. In this paper, we present an analysis of NMT and SMT systems' outputs from narrow domain English-Latvian MT systems that were trained on a rather small amount of data. We analyze post-edits produced by professional translators and manually annotated errors in these outputs. Analysis of post-edits allowed us to conclude that both approaches are comparably successful, allowing for an increase in translators' productivity, with the NMT system showing slightly worse results. Through the analysis of annotated errors, we found that NMT translations are more fluent than SMT translations. However, errors related to accuracy, especially, mistranslation and omission errors, occur more often in NMT outputs. The word form errors, that characterize the morphological richness of Latvian, are frequent for both systems, but slightly fewer in NMT outputs.

null

56,993

inproceedings

wang-etal-2017-towards

Towards Neural Machine Translation with Partially Aligned Corpora

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1039/

Wang, Yining and Zhao, Yang and Zhang, Jiajun and Zong, Chengqing and Xue, Zhengshan

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

384--393

While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs. In this paper, we address a new translation scenario in which there only exists monolingual corpora and phrase pairs. We propose a new method towards translation with partially aligned sentence pairs which are derived from the phrase pairs and monolingual corpora. To make full use of the partially aligned corpora, we adapt the conventional NMT training method in two aspects. On one hand, different generation strategies are designed for aligned and unaligned target words. On the other hand, a different objective function is designed to model the partially aligned parts. The experiments demonstrate that our method can achieve a relatively good result in such a translation scenario, and tiny bitexts can boost translation quality to a large extent.

null

56,994

inproceedings

lahiri-etal-2017-identifying

Identifying Usage Expression Sentences in Consumer Product Reviews

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1040/

Lahiri, Shibamouli and Vydiswaran, V.G.Vinod and Mihalcea, Rada

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

394--403

In this paper we introduce the problem of identifying usage expression sentences in a consumer product review. We create a human-annotated gold standard dataset of 565 reviews spanning five distinct product categories. Our dataset consists of more than 3,000 annotated sentences. We further introduce a classification system to label sentences according to whether or not they describe some {\textquotedblleft}usage{\textquotedblright}. The system combines lexical, syntactic, and semantic features in a product-agnostic fashion to yield good classification performance. We show the effectiveness of our approach using importance ranking of features, error analysis, and cross-product classification experiments.

null

56,995

inproceedings

asahara-kato-2017-reading

Between Reading Time and Syntactic/Semantic Categories

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1041/

Asahara, Masayuki and Kato, Sachi

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

404--412

This article presents a contrastive analysis between reading time and syntactic/semantic categories in Japanese. We overlaid the reading time annotation of BCCWJ-EyeTrack and a syntactic/semantic category information annotation on the {\textquoteleft}Balanced Corpus of Contemporary Written Japanese'. Statistical analysis based on a mixed linear model showed that verbal phrases tend to have shorter reading times than adjectives, adverbial phrases, or nominal phrases. The results suggest that the preceding phrases associated with the presenting phrases promote the reading process to shorten the gazing time.

null

56,996

inproceedings

ghaddar-langlais-2017-winer

{W}i{NER}: A {W}ikipedia Annotated Corpus for Named Entity Recognition

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1042/

Ghaddar, Abbas and Langlais, Phillippe

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

413--422

We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.

null

56,997

inproceedings

lakomkin-etal-2017-reusing

Reusing Neural Speech Representations for Auditory Emotion Recognition

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1043/

Lakomkin, Egor and Weber, Cornelius and Magg, Sven and Wermter, Stefan

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

423--430

Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcity of training data, general subjectivity in emotion perception resulting in low annotator agreement, and the uncertainty about which features are the most relevant and robust ones for classification. In this paper, we will tackle the latter problem. Inspired by the recent success of transfer learning methods we propose a set of architectures which utilize neural representations inferred by training on large speech databases for the acoustic emotion recognition task. Our experiments on the IEMOCAP dataset show {\textasciitilde}10{\%} relative improvements in the accuracy and F1-score over the baseline recurrent neural network which is trained end-to-end for emotion recognition.

null

56,998

inproceedings

tjandra-etal-2017-local

Local Monotonic Attention Mechanism for End-to-End Speech And Language Processing

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1044/

Tjandra, Andros and Sakti, Sakriani and Nakamura, Satoshi

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

431--440

Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the model to learn alignments between the source and the target sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in several tasks, such as automatic speech recognition (ASR), grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results on ASR, G2P and machine translation between two languages with similar sentence structures, demonstrate that the proposed encoder-decoder model with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.

null

56,999

inproceedings

salton-etal-2017-attentive

Attentive Language Models

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1045/

Salton, Giancarlo and Ross, Robert and Kelleher, John

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

441--450

In this paper, we extend Recurrent Neural Network Language Models (RNN-LMs) with an attention mechanism. We show that an {\textquotedblleft}attentive{\textquotedblright} RNN-LM (with 11M parameters) achieves a better perplexity than larger RNN-LMs (with 66M parameters) and achieves performance comparable to an ensemble of 10 similar sized RNN-LMs. We also show that an {\textquotedblleft}attentive{\textquotedblright} RNN-LM needs less contextual information to achieve similar results to the state-of-the-art on the wikitext2 dataset.

null

57,000

inproceedings

murawaki-2017-diachrony

Diachrony-aware Induction of Binary Latent Representations from Typological Features

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1046/

Murawaki, Yugo

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

451--461

Although features of linguistic typology are a promising alternative to lexical evidence for tracing evolutionary history of languages, a large number of missing values in the dataset pose serious difficulties for statistical modeling. In this paper, we combine two existing approaches to the problem: (1) the synchronic approach that focuses on interdependencies between features and (2) the diachronic approach that exploits phylogenetically- and/or spatially-related languages. Specifically, we propose a Bayesian model that (1) represents each language as a sequence of binary latent parameters encoding inter-feature dependencies and (2) relates a language`s parameters to those of its phylogenetic and spatial neighbors. Experiments show that the proposed model recovers missing values more accurately than others and that induced representations retain phylogenetic and spatial signals observed for surface features.

null

57,001

inproceedings

mostafazadeh-etal-2017-image

Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1047/

Mostafazadeh, Nasrin and Brockett, Chris and Dolan, Bill and Galley, Michel and Gao, Jianfeng and Spithourakis, Georgios and Vanderwende, Lucy

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

462--472

The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.

null

57,002

inproceedings

kobayashi-etal-2017-neural

A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1048/

Kobayashi, Sosuke and Okazaki, Naoaki and Inui, Kentaro

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

473--483

This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models. We proposed a method for on-the-fly construction and exploitation of word embeddings in both the input and output layers of a neural model by tracking contexts. This extends the dynamic entity representation used in Kobayashi et al. (2016) and incorporates a copy mechanism proposed independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we construct a new task and dataset called Anonymized Language Modeling for evaluating the ability to capture word meanings while reading. Experiments conducted using our novel dataset show that the proposed variant of RNN language model outperformed the baseline model. Furthermore, the experiments also demonstrate that dynamic updates of an output layer help a model predict reappearing entities, whereas those of an input layer are effective to predict words following reappearing entities.

null

57,003

inproceedings

shi-etal-2017-using

Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1049/

Shi, Wei and Yung, Frances and Rubino, Raphael and Demberg, Vera

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

484--495

Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional training data from parallel corpora: When humans translate a text, they sometimes add connectives (a process known as \textit{explicitation}). We automatically back-translate it into an English connective and use it to infer a label with high confidence. We show that a training set several times larger than the original training set can be generated this way. With the extra labeled instances, we show that even a simple bidirectional Long Short-Term Memory Network can outperform the current state-of-the-art.

null

57,004

inproceedings

wang-etal-2017-tag

Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1050/

Wang, Yizhong and Li, Sujian and Yang, Jingfeng and Sun, Xu and Wang, Houfeng

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

496--505

Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text. To tackle this task, recent studies have tried several deep learning methods but few of them exploited the syntactic information. In this work, we explore the idea of incorporating syntactic parse tree into neural networks. Specifically, we employ the Tree-LSTM model and Tree-GRU model, which is based on the tree structure, to encode the arguments in a relation. And we further leverage the constituent tags to control the semantic composition process in these tree-structured neural networks. Experimental results show that our method achieves state-of-the-art performance on PDTB corpus.

null

57,005

inproceedings

abdalla-hirst-2017-cross

Cross-Lingual Sentiment Analysis Without (Good) Translation

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1051/

Abdalla, Mohamed and Hirst, Graeme

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

506--515

Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.

null

57,006

inproceedings

gao-etal-2017-implicit

Implicit Syntactic Features for Target-dependent Sentiment Analysis

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1052/

Gao, Yuze and Zhang, Yue and Xiao, Tong

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

516--524

Targeted sentiment analysis investigates the sentiment polarities on given target mentions from input texts. Different from sentence level sentiment, it offers more fine-grained knowledge on each entity mention. While early work leveraged syntactic information, recent research has used neural representation learning to induce features automatically, thereby avoiding error propagation of syntactic parsers, which are particularly severe on social media texts. We study a method to leverage syntactic information without explicitly building the parser outputs, by training an encoder-decoder structure parser model on standard syntactic treebanks, and then leveraging its hidden encoder layers when analysing tweets. Such hidden vectors do not contain explicit syntactic outputs, yet encode rich syntactic features. We use them to augment the inputs to a baseline state-of-the-art targeted sentiment classifier, observing significant improvements on various benchmark datasets. We obtain the best accuracies on all test sets.

null

57,007

inproceedings

tamilselvam-etal-2017-graph

Graph Based Sentiment Aggregation using {C}oncept{N}et Ontology

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1053/

Tamilselvam, Srikanth and Nagar, Seema and Mishra, Abhijit and Dey, Kuntal

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

525--535

The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text. The current paper addresses the sentiment aggregation problem, by assigning weights to each aspect appearing in the user-generated content, that are proportionate to the strategic importance of the aspect in the pragmatic domain. The novelty of this paper is in computing the pragmatic significance (weight) of each aspect, using graph centrality measures (applied on domain specific ontology-graphs extracted from ConceptNet), and deeply ingraining these weights while aggregating the sentiments from opinionated text. We experiment over multiple real-life product review data. Our system consistently outperforms the state of the art - by as much as a F-score of 20.39{\%} in one case.

null

57,008

inproceedings

nguyen-nguyen-2017-sentence

Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1054/

Nguyen, Huy Thanh and Nguyen, Minh Le

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

536--544

Tweet-level sentiment classification in Twitter social networking has many challenges: exploiting syntax, semantic, sentiment, and context in tweets. To address these problems, we propose a novel approach to sentiment analysis that uses lexicon features for building lexicon embeddings (LexW2Vs) and generates character attention vectors (CharAVs) by using a Deep Convolutional Neural Network (DeepCNN). Our approach integrates LexW2Vs and CharAVs with continuous word embeddings (ContinuousW2Vs) and dependency-based word embeddings (DependencyW2Vs) simultaneously in order to increase information for each word into a Bidirectional Contextual Gated Recurrent Neural Network (Bi-CGRNN). We evaluate our model on two Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.

null

57,009

inproceedings

jin-etal-2017-combining

Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1055/

Jin, Yiping and Wanvarie, Dittaya and Le, Phu

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

545--554

In this paper we propose a lightly-supervised framework to rapidly build text classifiers for contextual advertising. Traditionally text classification techniques require labeled training documents for each predefined class. In the scenario of contextual advertising, advertisers often want to target to a specific class of webpages most relevant to their product or service, which may not be covered by a pre-trained classifier. Moreover, the advertisers are interested in whether a webpage is {\textquotedblleft}relevant{\textquotedblright} or {\textquotedblleft}irrelevant{\textquotedblright}. It is time-consuming to solicit the advertisers for reliable training signals for the negative class. Therefore, it is more suitable to model the problem as a one-class classification problem, in contrast to traditional classification problems where disjoint classes are defined a priori. We first apply two state-of-the-art lightly-supervised classification models, generalized expectation (GE) criteria (Druck et al., 2008) and multinomial naive Bayes (MNB) with priors (Settles, 2011) to one-class classification where the user only needs to provide a small list of labeled words for the target class. To combine the strengths of the two models, we fuse them together by using MNB to automatically enrich the constraints for GE training. We also explore ensemble method to combine classifiers. On a corpus of webpages from real-time bidding requests, the proposed model achieves the highest average F1 of 0.69 and closes more than half of the gap between previous state-of-the-art lightly-supervised models to a fully-supervised MaxEnt model.

null

57,010

inproceedings

liu-etal-2017-capturing

Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1056/

Liu, Fei and Baldwin, Timothy and Cohn, Trevor

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

555--565

Despite successful applications across a broad range of NLP tasks, conditional random fields ({\textquotedblleft}CRFs{\textquotedblright}), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items. Attempts to extend CRFs to capture long-range dependencies have largely come at the cost of computational complexity and approximate inference. In this work, we propose an extension to CRFs by integrating external memory, taking inspiration from memory networks, thereby allowing CRFs to incorporate information far beyond neighbouring steps. Experiments across two tasks show substantial improvements over strong CRF and LSTM baselines.

null

57,011

inproceedings

tran-etal-2017-named

Named Entity Recognition with Stack Residual {LSTM} and Trainable Bias Decoding

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1057/

Tran, Quan and MacKinlay, Andrew and Jimeno Yepes, Antonio

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

566--575

Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.

null

57,012

inproceedings

das-ghosh-2017-neuramanteau

{N}euramanteau: A Neural Network Ensemble Model for Lexical Blends

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1058/

Das, Kollol and Ghosh, Shaona

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

576--583

The problem of blend formation in generative linguistics is interesting in the context of neologism, their quick adoption in modern life and the creative generative process guiding their formation. Blend quality depends on multitude of factors with high degrees of uncertainty. In this work, we investigate if the modern neural network models can sufficiently capture and recognize the creative blend composition process. We propose recurrent neural network sequence-to-sequence models, that are evaluated on multiple blend datasets available in the literature. We propose an ensemble neural and hybrid model that outperforms most of the baselines and heuristic models upon evaluation on test data.

null

57,013

inproceedings

ferracane-etal-2017-leveraging

Leveraging Discourse Information Effectively for Authorship Attribution

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1059/

Ferracane, Elisa and Wang, Su and Mooney, Raymond

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

584--593

We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution. We present a novel method to embed discourse features in a Convolutional Neural Network text classifier, which achieves a state-of-the-art result by a significant margin. We empirically investigate several featurization methods to understand the conditions under which discourse features contribute non-trivial performance gains, and analyze discourse embeddings.

null

57,014

inproceedings

persing-ng-2017-lightly

Lightly-Supervised Modeling of Argument Persuasiveness

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1060/

Persing, Isaac and Ng, Vincent

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

594--604

We propose the first lightly-supervised approach to scoring an argument`s persuasiveness. Key to our approach is the novel hypothesis that lightly-supervised persuasiveness scoring is possible by explicitly modeling the major errors that negatively impact persuasiveness. In an evaluation on a new annotated corpus of online debate arguments, our approach rivals its fully-supervised counterparts in performance by four scoring metrics when using only 10{\%} of the available training instances.

null

57,015

inproceedings

luan-etal-2017-multi

Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1061/

Luan, Yi and Brockett, Chris and Dolan, Bill and Gao, Jianfeng and Galley, Michel

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

605--614

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.

null

57,016

inproceedings

mehri-carenini-2017-chat

Chat Disentanglement: Identifying Semantic Reply Relationships with Random Forests and Recurrent Neural Networks

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1062/

Mehri, Shikib and Carenini, Giuseppe

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

615--623

Thread disentanglement is a precursor to any high-level analysis of multiparticipant chats. Existing research approaches the problem by calculating the likelihood of two messages belonging in the same thread. Our approach leverages a newly annotated dataset to identify reply relationships. Furthermore, we explore the usage of an RNN, along with large quantities of unlabeled data, to learn semantic relationships between messages. Our proposed pipeline, which utilizes a reply classifier and an RNN to generate a set of disentangled threads, is novel and performs well against previous work.

null

57,017

inproceedings

schulder-etal-2017-towards

Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1063/

Schulder, Marc and Wiegand, Michael and Ruppenhofer, Josef and Roth, Benjamin

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

624--633

We present a major step towards the creation of the first high-coverage lexicon of polarity shifters. In this work, we bootstrap a lexicon of verbs by exploiting various linguistic features. Polarity shifters, such as {\textquotedblleft}abandon{\textquotedblright}, are similar to negations (e.g. {\textquotedblleft}not{\textquotedblright}) in that they move the polarity of a phrase towards its inverse, as in {\textquotedblleft}abandon all hope{\textquotedblright}. While there exist lists of negation words, creating comprehensive lists of polarity shifters is far more challenging due to their sheer number. On a sample of manually annotated verbs we examine a variety of linguistic features for this task. Then we build a supervised classifier to increase coverage. We show that this approach drastically reduces the annotation effort while ensuring a high-precision lexicon. We also show that our acquired knowledge of verbal polarity shifters improves phrase-level sentiment analysis.

null

57,018

inproceedings

ma-etal-2017-cascading

Cascading Multiway Attentions for Document-level Sentiment Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1064/

Ma, Dehong and Li, Sujian and Zhang, Xiaodong and Wang, Houfeng and Sun, Xu

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

634--643

Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not comprehensively consider what roles the three kinds of information play in text modeling. In this paper, to reasonably use all the information, we present the idea that user, product and their combination can all influence the generation of attentions to words and sentences, when judging the sentiment of a document. With this idea, we propose a cascading multiway attention (CMA) model, where multiple ways of using user and product information are cascaded to influence the generation of attentions on the word and sentence layers. Then, sentences and documents are well modeled by multiple representation vectors, which provide rich information for sentiment classification. Experiments on IMDB and Yelp datasets demonstrate the effectiveness of our model.

null

57,019

inproceedings

nguyen-nguyen-2017-ensemble

An Ensemble Method with Sentiment Features and Clustering Support

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1065/

Nguyen, Huy Tien and Nguyen, Minh Le

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

644--653

Deep learning models have recently been applied successfully in natural language processing, especially sentiment analysis. Each deep learning model has a particular advantage, but it is difficult to combine these advantages into one model, especially in the area of sentiment analysis. In our approach, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) were utilized to learn sentiment-specific features in a freezing scheme. This scenario provides a novel and efficient way for integrating advantages of deep learning models. In addition, we also grouped documents into clusters by their similarity and applied the prediction score of Naive Bayes SVM (NBSVM) method to boost the classification accuracy of each group. The experiments show that our method achieves the state-of-the-art performance on two well-known datasets: IMDB large movie reviews for document level and Pang {\&} Lee movie reviews for sentence level.

null

57,020

inproceedings

yu-jiang-2017-leveraging

Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1066/

Yu, Jianfei and Jiang, Jing

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

654--663

In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.

null

57,021

inproceedings

wilson-mihalcea-2017-measuring

Measuring Semantic Relations between Human Activities

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1067/

Wilson, Steven and Mihalcea, Rada

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

664--673

The things people do in their daily lives can provide valuable insights into their personality, values, and interests. Unstructured text data on social media platforms are rich in behavioral content, and automated systems can be deployed to learn about human activity on a broad scale if these systems are able to reason about the content of interest. In order to aid in the evaluation of such systems, we introduce a new phrase-level semantic textual similarity dataset comprised of human activity phrases, providing a testbed for automated systems that analyze relationships between phrasal descriptions of people`s actions. Our set of 1,000 pairs of activities is annotated by human judges across four relational dimensions including similarity, relatedness, motivational alignment, and perceived actor congruence. We evaluate a set of strong baselines for the task of generating scores that correlate highly with human ratings, and we introduce several new approaches to the phrase-level similarity task in the domain of human activities.

null

57,022

inproceedings

min-etal-2017-learning

Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1068/

Min, Bonan and Jiang, Zhuolin and Freedman, Marjorie and Weischedel, Ralph

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

674--684

Typically, relation extraction models are trained to extract instances of a relation ontology using only training data from a single language. However, the concepts represented by the relation ontology (e.g. ResidesIn, EmployeeOf) are language independent. The numbers of annotated examples available for a given ontology vary between languages. For example, there are far fewer annotated examples in Spanish and Japanese than English and Chinese. Furthermore, using only language-specific training data results in the need to manually annotate equivalently large amounts of training for each new language a system encounters. We propose a deep neural network to learn transferable, discriminative bilingual representation. Experiments on the ACE 2005 multilingual training corpus demonstrate that the joint training process results in significant improvement in relation classification performance over the monolingual counterparts. The learnt representation is discriminative and transferable between languages. When using 10{\%} (25K English words, or 30K Chinese characters) of the training data, our approach results in doubling F1 compared to a monolingual baseline. We achieve comparable performance to the monolingual system trained with 250K English words (or 300K Chinese characters) With 50{\%} of training data.

null

57,023

inproceedings

hazem-morin-2017-bilingual

Bilingual Word Embeddings for Bilingual Terminology Extraction from Specialized Comparable Corpora

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1069/

Hazem, Amir and Morin, Emmanuel

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

685--693

Bilingual lexicon extraction from comparable corpora is constrained by the small amount of available data when dealing with specialized domains. This aspect penalizes the performance of distributional-based approaches, which is closely related to the reliability of word`s cooccurrence counts extracted from comparable corpora. A solution to avoid this limitation is to associate external resources with the comparable corpus. Since bilingual word embeddings have recently shown efficient models for learning bilingual distributed representation of words, we explore different word embedding models and show how a general-domain comparable corpus can enrich a specialized comparable corpus via neural networks

null

57,024

inproceedings

liu-nouvel-2017-bambara

A {B}ambara Tonalization System for Word Sense Disambiguation Using Differential Coding, Segmentation and Edit Operation Filtering

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1070/

Liu, Luigi Yu-Cheng and Nouvel, Damien

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

694--703

In many languages such as Bambara or Arabic, tone markers (diacritics) may be written but are actually often omitted. NLP applications are confronted to ambiguities and subsequent difficulties when processing texts. To circumvent this problem, tonalization may be used, as a word sense disambiguation task, relying on context to add diacritics that partially disambiguate words as well as senses. In this paper, we describe our implementation of a Bambara tonalizer that adds tone markers using machine learning (CRFs). To make our tool efficient, we used differential coding, word segmentation and edit operation filtering. We describe our approach that allows tractable machine learning and improves accuracy: our model may be learned within minutes on a 358K-word corpus and reaches 92.3{\%} accuracy.

null

57,025

inproceedings

zhao-kawahara-2017-joint

Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1071/

Zhao, Tianyu and Kawahara, Tatsuya

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

704--712

Dialog act segmentation and recognition are basic natural language understanding tasks in spoken dialog systems. This paper investigates a unified architecture for these two tasks, which aims to improve the model`s performance on both of the tasks. Compared with past joint models, the proposed architecture can (1) incorporate contextual information in dialog act recognition, and (2) integrate models for tasks of different levels as a whole, i.e. dialog act segmentation on the word level and dialog act recognition on the segment level. Experimental results show that the joint training system outperforms the simple cascading system and the joint coding system on both dialog act segmentation and recognition tasks.

null

57,026

inproceedings

wang-etal-2017-predicting

Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1072/

Wang, Xin and Wang, Jianan and Liu, Yuanchao and Wang, Xiaolong and Wang, Zhuoran and Wang, Baoxun

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

713--722

User experience is essential for human-computer dialogue systems. However, it is impractical to ask users to provide explicit feedbacks when the agents' responses displease them. Therefore, in this paper, we explore to predict users' imminent dissatisfactions caused by intelligent agents by analysing the existing utterances in the dialogue sessions. To our knowledge, this is the first work focusing on this task. Several possible factors that trigger negative emotions are modelled. A relation sequence model (RSM) is proposed to encode the sequence of appropriateness of current response with respect to the earlier utterances. The experimental results show that the proposed structure is effective in modelling emotional risk (possibility of negative feedback) than existing conversation modelling approaches. Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work. Balanced sampling with respect to the last response in the distance supervision data are shown to be reliable for data augmentation.

null

57,027

inproceedings

madan-joshi-2017-finding

Finding Dominant User Utterances And System Responses in Conversations

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1073/

Madan, Dhiraj and Joshi, Sachindra

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

723--732

There are several dialog frameworks which allow manual specification of intents and rule based dialog flow. The rule based framework provides good control to dialog designers at the expense of being more time consuming and laborious. The job of a dialog designer can be reduced if we could identify pairs of user intents and corresponding responses automatically from prior conversations between users and agents. In this paper we propose an approach to find these frequent user utterances (which serve as examples for intents) and corresponding agent responses. We propose a novel SimCluster algorithm that extends standard K-means algorithm to simultaneously cluster user utterances and agent utterances by taking their adjacency information into account. The method also aligns these clusters to provide pairs of intents and response groups. We compare our results with those produced by using simple Kmeans clustering on a real dataset and observe upto 10{\%} absolute improvement in F1-scores. Through our experiments on synthetic dataset, we show that our algorithm gains more advantage over K-means algorithm when the data has large variance.

null

57,028

inproceedings

li-etal-2017-end

End-to-End Task-Completion Neural Dialogue Systems

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1074/

Li, Xiujun and Chen, Yun-Nung and Li, Lihong and Gao, Jianfeng and Celikyilmaz, Asli

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

733--743

One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.

null

57,029

inproceedings

lau-etal-2017-end

End-to-end Network for {T}witter Geolocation Prediction and Hashing

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1075/

Lau, Jey Han and Chi, Lianhua and Tran, Khoi-Nguyen and Cohn, Trevor

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

744--753

We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2{\%}-6{\%}. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.

null

57,030

inproceedings

newell-etal-2017-assessing

Assessing the Verifiability of Attributions in News Text

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1076/

Newell, Edward and Schang, Ariane and Margolin, Drew and Ruths, Derek

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

754--763

When reporting the news, journalists rely on the statements of stakeholders, experts, and officials. The attribution of such a statement is verifiable if its fidelity to the source can be confirmed or denied. In this paper, we develop a new NLP task: determining the verifiability of an attribution based on linguistic cues. We operationalize the notion of verifiability as a score between 0 and 1 using human judgments in a comparison-based approach. Using crowdsourcing, we create a dataset of verifiability-scored attributions, and demonstrate a model that achieves an RMSE of 0.057 and Spearman`s rank correlation of 0.95 to human-generated scores. We discuss the application of this technique to the analysis of mass media.

null

57,031

inproceedings

rieman-etal-2017-domain

Domain Adaptation from User-level {F}acebook Models to County-level {T}witter Predictions

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1077/

Rieman, Daniel and Jaidka, Kokil and Schwartz, H. Andrew and Ungar, Lyle

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

764--773

Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires. It is challenging to apply these models to make general predictions about attributes of communities, such as personality distributions across US counties, because it requires 1. the potentially inavailability of the original training data because of privacy and ethical regulations, 2. adapting Facebook language models to Twitter language without retraining the model, and 3. adapting from users to county-level collections of tweets. We propose a two-step algorithm, Target Side Domain Adaptation (TSDA) for such domain adaptation when no labeled Twitter/county data is available. TSDA corrects for the different word distributions between Facebook and Twitter and for the varying word distributions across counties by adjusting target side word frequencies; no changes to the trained model are made. In the case of predicting the Big Five county-level personality traits, TSDA outperforms a state-of-the-art domain adaptation method, gives county-level predictions that have fewer extreme outliers, higher year-to-year stability, and higher correlation with county-level outcomes.

null

57,032

inproceedings

gao-etal-2017-recognizing

Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1078/

Gao, Lei and Kuppersmith, Alexis and Huang, Ruihong

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

774--782

In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.

null

57,033

inproceedings

ni-etal-2017-estimating

Estimating Reactions and Recommending Products with Generative Models of Reviews

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1079/

Ni, Jianmo and Lipton, Zachary C. and Vikram, Sharad and McAuley, Julian

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

783--791

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.

null

57,034

inproceedings

ishigaki-etal-2017-summarizing

Summarizing Lengthy Questions

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1080/

Ishigaki, Tatsuya and Takamura, Hiroya and Okumura, Manabu

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

792--800

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.

null

57,035

inproceedings

falke-etal-2017-concept

Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1081/

Falke, Tobias and Meyer, Christian M. and Gurevych, Iryna

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

801--811

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.

null

57,036

inproceedings

j-kurisinkel-etal-2017-abstractive

Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1082/

J Kurisinkel, Litton and Zhang, Yue and Varma, Vasudeva

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

812--821

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.

null

57,037

inproceedings

judea-strube-2017-event

Event Argument Identification on Dependency Graphs with Bidirectional {LSTM}s

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1083/

Judea, Alex and Strube, Michael

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

822--831

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.

null

57,038

inproceedings

zhang-etal-2017-selective

Selective Decoding for Cross-lingual Open Information Extraction

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1084/

Zhang, Sheng and Duh, Kevin and Van Durme, Benjamin

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

832--842

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.

null

57,039

inproceedings

mcdowell-etal-2017-event

Event Ordering with a Generalized Model for Sieve Prediction Ranking

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1085/

McDowell, Bill and Chambers, Nathanael and Ororbia II, Alexander and Reitter, David

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

843--853

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.

null

57,040

inproceedings

yu-etal-2017-open

Open Relation Extraction and Grounding

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1086/

Yu, Dian and Huang, Lifu and Ji, Heng

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

854--864

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.

null

57,041

inproceedings

you-etal-2017-extraction

Extraction of Gene-Environment Interaction from the Biomedical Literature

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1087/

You, Jinseon and Chung, Jin-Woo and Yang, Wonsuk and Park, Jong C.

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

865--874

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.

null

57,042

inproceedings

pan-etal-2017-course

Course Concept Extraction in {MOOC}s via Embedding-Based Graph Propagation

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1088/

Pan, Liangming and Wang, Xiaochen and Li, Chengjiang and Li, Juanzi and Tang, Jie

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

875--884

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{\ensuremath{<}}{\ensuremath{<}}0.01, t-test).

null

57,043

inproceedings

perez-rosas-etal-2017-identity

Identity Deception Detection

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1089/

P{\'e}rez-Rosas, Ver{\'o}nica and Davenport, Quincy and Dai, Anna Mengdan and Abouelenien, Mohamed and Mihalcea, Rada

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

885--894

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.

null

57,044

inproceedings

ling-etal-2017-learning

Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1090/

Ling, Yuan and Hasan, Sadid A. and Datla, Vivek and Qadir, Ashequl and Lee, Kathy and Liu, Joey and Farri, Oladimeji

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

895--905

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.

null

57,045

inproceedings

brad-etal-2017-dataset

Dataset for a Neural Natural Language Interface for Databases ({NNLIDB})

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1091/

Brad, Florin and Iacob, Radu Cristian Alexandru and Hosu, Ionel Alexandru and Rebedea, Traian

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

906--914

Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability. Moreover, the lack of a large corpus to be used as a standard benchmark has made data-driven approaches difficult to develop and compare. In this paper, we revisit the problem of NLIDBs and recast it as a sequence translation problem. To this end, we introduce a large dataset extracted from the Stack Exchange Data Explorer website, which can be used for training neural natural language interfaces for databases. We also report encouraging baseline results on a smaller manually annotated test corpus, obtained using an attention-based sequence-to-sequence neural network.

null

57,046

inproceedings

pragst-etal-2017-acquisition

Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1092/

Pragst, Louisa and Yoshino, Koichiro and Minker, Wolfgang and Nakamura, Satoshi and Ultes, Stefan

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

915--925

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.

null

57,047

inproceedings

hasanuzzaman-etal-2017-demographic

Demographic Word Embeddings for Racism Detection on {T}witter

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1093/

Hasanuzzaman, Mohammed and Dias, Ga{\"el and Way, Andy

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

926--936

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.

null

57,048

inproceedings

saito-etal-2017-automatically

Automatically Extracting Variant-Normalization Pairs for {J}apanese Text Normalization

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1094/

Saito, Itsumi and Nishida, Kyosuke and Sadamitsu, Kugatsu and Saito, Kuniko and Tomita, Junji

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

937--946

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.

null

57,049

inproceedings

zhu-etal-2017-semantic

Semantic Document Distance Measures and Unsupervised Document Revision Detection

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1095/

Zhu, Xiaofeng and Klabjan, Diego and Bless, Patrick

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

947--956

In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED). Our revision detection system is designed for a large scale corpus and implemented in Apache Spark. We demonstrate that our system can more precisely detect revisions than state-of-the-art methods by utilizing the Wikipedia revision dumps and simulated data sets.

null

57,050

inproceedings

shen-etal-2017-empirical

An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1096/

Shen, Yelong and Liu, Xiaodong and Duh, Kevin and Gao, Jianfeng

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

957--966

Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. {\%}across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.

null

57,051

inproceedings

kobayashi-etal-2017-automated

Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1097/

Kobayashi, Mio and Ishii, Ai and Hoshino, Chikara and Miyashita, Hiroshi and Matsuzaki, Takuya

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

967--975

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.

null

57,052

inproceedings

hsiao-etal-2017-integrating

Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1098/

Hsiao, Wei-Chuan and Huang, Hen-Hsen and Chen, Hsin-Hsi

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

976--985

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.

null

57,053

inproceedings

li-etal-2017-dailydialog

{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1099/

Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

986--995

We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}

null

57,054

inproceedings

white-etal-2017-inference

Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1100/

White, Aaron Steven and Rastogi, Pushpendre and Duh, Kevin and Van Durme, Benjamin

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

996--1005

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.

null

57,055

inproceedings

dhondt-etal-2017-generating

Generating a Training Corpus for {OCR} Post-Correction Using Encoder-Decoder Model

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1101/

D{'}hondt, Eva and Grouin, Cyril and Grau, Brigitte

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

1006--1014

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.

null

57,056

inproceedings

pappas-popescu-belis-2017-multilingual

Multilingual Hierarchical Attention Networks for Document Classification

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1102/

Pappas, Nikolaos and Popescu-Belis, Andrei

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

1015--1025

Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language. However, when multilingual document collections are considered, training such models separately for each language entails linear parameter growth and lack of cross-language transfer. Learning a single multilingual model with fewer parameters is therefore a challenging but potentially beneficial objective. To this end, we propose multilingual hierarchical attention networks for learning document structures, with shared encoders and/or shared attention mechanisms across languages, using multi-task learning and an aligned semantic space as input. We evaluate the proposed models on multilingual document classification with disjoint label sets, on a large dataset which we provide, with 600k news documents in 8 languages, and 5k labels. The multilingual models outperform monolingual ones in low-resource as well as full-resource settings, and use fewer parameters, thus confirming their computational efficiency and the utility of cross-language transfer.

null

57,057

inproceedings

maki-etal-2017-roles

Roles and Success in {W}ikipedia Talk Pages: Identifying Latent Patterns of Behavior

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-1103/

Maki, Keith and Yoder, Michael and Jo, Yohan and Ros{\'e}, Carolyn

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

1026--1035

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.

null

57,058

inproceedings

watanabe-etal-2017-cky

{CKY}-based Convolutional Attention for Neural Machine Translation

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2001/

Watanabe, Taiki and Tamura, Akihiro and Ninomiya, Takashi

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

1--6

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.

null

57,060

inproceedings

kamigaito-etal-2017-supervised

Supervised Attention for Sequence-to-Sequence Constituency Parsing

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2002/

Kamigaito, Hidetaka and Hayashi, Katsuhiko and Hirao, Tsutomu and Takamura, Hiroya and Okumura, Manabu and Nagata, Masaaki

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

7--12

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.

null

57,061

inproceedings

aminian-etal-2017-transferring

Transferring Semantic Roles Using Translation and Syntactic Information

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2003/

Aminian, Maryam and Rasooli, Mohammad Sadegh and Diab, Mona

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

13--19

Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel data. We propose a transfer method that employs information from source and target syntactic dependencies as well as word alignment density to improve the quality of an iterative bootstrapping method. Our experiments yield a 3.5 absolute labeled F-score improvement over a standard annotation projection method.

null

57,062

inproceedings

khayrallah-etal-2017-neural

Neural Lattice Search for Domain Adaptation in Machine Translation

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2004/

Khayrallah, Huda and Kumar, Gaurav and Duh, Kevin and Post, Matt and Koehn, Philipp

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

20--25

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.

null

57,063

inproceedings

yawata-etal-2017-analyzing

Analyzing Well-Formedness of Syllables in {J}apanese {S}ign {L}anguage

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2005/

Yawata, Satoshi and Miwa, Makoto and Sasaki, Yutaka and Hara, Daisuke

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

26--30

This paper tackles a problem of analyzing the well-formedness of syllables in Japanese Sign Language (JSL). We formulate the problem as a classification problem that classifies syllables into well-formed or ill-formed. We build a data set that contains hand-coded syllables and their well-formedness. We define a fine-grained feature set based on the hand-coded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained classifier. We also perform pseudo active learning to investigate the applicability of active learning in analyzing syllables. In the experiments, the best classifier with our combinatorial features achieved the accuracy of 87.0{\%}. The pseudo active learning is also shown to be effective showing that it could reduce about 84{\%} of training instances to achieve the accuracy of 82.0{\%} when compared to the model without active learning.

null

57,064

inproceedings

patel-bhattacharyya-2017-towards

Towards Lower Bounds on Number of Dimensions for Word Embeddings

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2006/

Patel, Kevin and Bhattacharyya, Pushpak

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

31--36

Word embeddings are a relatively new addition to the modern NLP researcher`s toolkit. However, unlike other tools, word embeddings are used in a black box manner. There are very few studies regarding various hyperparameters. One such hyperparameter is the dimension of word embeddings. They are rather decided based on a rule of thumb: in the range 50 to 300. In this paper, we show that the dimension should instead be chosen based on corpus statistics. More specifically, we show that the number of pairwise equidistant words of the corpus vocabulary (as defined by some distance/similarity metric) gives a lower bound on the the number of dimensions , and going below this bound results in degradation of quality of learned word embeddings. Through our evaluations on standard word embedding evaluation tasks, we show that for dimensions higher than or equal to the bound, we get better results as compared to the ones below it.

null

57,065

inproceedings

nguyen-etal-2017-sequence

Sequence to Sequence Learning for Event Prediction

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2007/

Nguyen, Dai Quoc and Nguyen, Dat Quoc and Chu, Cuong Xuan and Thater, Stefan and Pinkal, Manfred

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

37--42

This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.

null

57,066

inproceedings

takase-etal-2017-input

Input-to-Output Gate to Improve {RNN} Language Models

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2008/

Takase, Sho and Suzuki, Jun and Nagata, Masaaki

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

43--48

This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple structure, and thus, can be easily combined with any RNN language models. Our experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG consistently boosts the performance of several different types of current topline RNN language models.

null

57,067

inproceedings

arnold-etal-2017-counterfactual

Counterfactual Language Model Adaptation for Suggesting Phrases

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2009/

Arnold, Kenneth and Chang, Kai-Wei and Kalai, Adam

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

49--54

Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability.

null

57,068

inproceedings

liang-shu-2017-deep

Deep Automated Multi-task Learning

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2010/

Liang, Davis and Shu, Yan

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

55--60

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character prediction, and missing word completion as potential automated tasks. Our results show that training on a primary task in parallel with a secondary automated task improves both the convergence speed and accuracy for the primary task. We suggest two methods for augmenting an existing network with automated tasks and establish better performance in topic prediction, sentiment analysis, and hashtag recommendation. Finally, we show that the MTL models can perform well on datasets that are small and colloquial by nature.

null

57,069

inproceedings

soni-etal-2017-post

Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2011/

Soni, Akshay and Pappu, Aasish and Ni, Jerry Chia-mau and Chevalier, Troy

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

61--66

In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set. In this paper, we present a suite of {--} MLL algorithm independent {--} post-processing techniques that utilize the conditional and directional label-dependences in order to make the predictions from any MLL approach more coherent and precise. We solve constraint optimization problem over the output produced by any MLL approach and the result is a refined version of the input predicted label set. Using proposed techniques, we show absolute improvement of 3{\%} on English News and 10{\%} on Chinese E-commerce datasets for P@K metric.

null

57,070

inproceedings

beck-cohn-2017-learning

Learning Kernels over Strings using {G}aussian Processes

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2012/

Beck, Daniel and Cohn, Trevor

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

67--73

Non-contiguous word sequences are widely known to be important in modelling natural language. However they not explicitly encoded in common text representations. In this work we propose a model for text processing using string kernels, capable of flexibly representing non-contiguous sequences. Specifically, we derive a vectorised version of the string kernel algorithm and their gradients, allowing efficient hyperparameter optimisation as part of a Gaussian Process framework. Experiments on synthetic data and text regression for emotion analysis show the promise of this technique.

null

57,071

inproceedings

fujinuma-grissom-ii-2017-substring

Substring Frequency Features for Segmentation of {J}apanese Katakana Words with Unlabeled Corpora

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2013/

Fujinuma, Yoshinari and Grissom II, Alvin

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

74--79

Word segmentation is crucial in natural language processing tasks for unsegmented languages. In Japanese, many out-of-vocabulary words appear in the phonetic syllabary katakana, making segmentation more difficult due to the lack of clues found in mixed script settings. In this paper, we propose a straightforward approach based on a variant of tf-idf and apply it to the problem of word segmentation in Japanese. Even though our method uses only an unlabeled corpus, experimental results show that it achieves performance comparable to existing methods that use manually labeled corpora. Furthermore, it improves performance of simple word segmentation models trained on a manually labeled corpus.

null

57,072

inproceedings

hsieh-etal-2017-monpa

{MONPA}: Multi-objective Named-entity and Part-of-speech Annotator for {C}hinese using Recurrent Neural Network

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2014/

Hsieh, Yu-Lun and Chang, Yung-Chun and Huang, Yi-Jie and Yeh, Shu-Hao and Chen, Chun-Hung and Hsu, Wen-Lian

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

80--85

Part-of-speech (POS) tagging and named entity recognition (NER) are crucial steps in natural language processing. In addition, the difficulty of word segmentation places additional burden on those who intend to deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an end-to-end model using character-based recurrent neural network (RNN) to jointly accomplish segmentation, POS tagging and NER of a Chinese sentence. Experiments on previous word segmentation and NER datasets show that a single model with the proposed architecture is comparable to those trained specifically for each task, and outperforms freely-available softwares. Moreover, we provide a web-based interface for the public to easily access this resource.

null

57,073

inproceedings

shao-etal-2017-recall

Recall is the Proper Evaluation Metric for Word Segmentation

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2015/

Shao, Yan and Hardmeier, Christian and Nivre, Joakim

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

86--90

We extensively analyse the correlations and drawbacks of conventionally employed evaluation metrics for word segmentation. Unlike in standard information retrieval, precision favours under-splitting systems and therefore can be misleading in word segmentation. Overall, based on both theoretical and experimental analysis, we propose that precision should be excluded from the standard evaluation metrics and that the evaluation score obtained by using only recall is sufficient and better correlated with the performance of word segmentation systems.

null

57,074

inproceedings

cotterell-duh-2017-low

Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2016/

Cotterell, Ryan and Duh, Kevin

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

91--96

Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world`s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows knowledge transfer from the high-resource languages to the low-resource ones, improving F1 by up to 9.8 points.

null

57,075

inproceedings

sato-etal-2017-segment

Segment-Level Neural Conditional Random Fields for Named Entity Recognition

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2017/

Sato, Motoki and Shindo, Hiroyuki and Yamada, Ikuya and Matsumoto, Yuji

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

97--102

We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.

null

57,076

inproceedings

kehat-pustejovsky-2017-integrating

Integrating Vision and Language Datasets to Measure Word Concreteness

Kondrak, Greg and Watanabe, Taro

nov

2017

Taipei, Taiwan

Asian Federation of Natural Language Processing

https://aclanthology.org/I17-2018/

Kehat, Gitit and Pustejovsky, James

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

103--108

We present and take advantage of the inherent visualizability properties of words in visual corpora (the textual components of vision-language datasets) to compute concreteness scores for words. Our simple method does not require hand-annotated concreteness score lists for training, and yields state-of-the-art results when evaluated against concreteness scores lists and previously derived scores, as well as when used for metaphor detection.

null

57,077