Tsuyoshi Okita


2015

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The DCU Discourse Parser: A Sense Classification Task
Tsuyoshi Okita | Longyue Wang | Qun Liu
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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The DCU Discourse Parser for Connective, Argument Identification and Explicit Sense Classification
Longyue Wang | Chris Hokamp | Tsuyoshi Okita | Xiaojun Zhang | Qun Liu
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

2014

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DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task
Xiaofeng Wu | Rejwanul Haque | Tsuyoshi Okita | Piyush Arora | Andy Way | Qun Liu
Proceedings of the Ninth Workshop on Statistical Machine Translation

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DCU Terminology Translation System for Medical Query Subtask at WMT14
Tsuyoshi Okita | Ali Vahid | Andy Way | Qun Liu
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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Shallow Semantically-Informed PBSMT and HPBSMT
Tsuyoshi Okita | Qun Liu | Josef van Genabith
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT
Josef van Genabith | Toni Badia | Christian Federmann | Maite Melero | Marta R. Costa-jussà | Tsuyoshi Okita
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

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System Combination with Extra Alignment Information
Xiaofeng Wu | Tsuyoshi Okita | Josef van Genabith | Qun Liu
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

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Topic Modeling-based Domain Adaptation for System Combination
Tsuyoshi Okita | Antonio Toral | Josef van Genabith
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

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Sentence-Level Quality Estimation for MT System Combination
Tsuyoshi Okita | Raphaël Rubino | Josef van Genabith
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

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Neural Probabilistic Language Model for System Combination
Tsuyoshi Okita
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

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Results from the ML4HMT-12 Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation
Christian Federmann | Tsuyoshi Okita | Maite Melero | Marta R. Costa-Jussa | Toni Badia | Josef van Genabith
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

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Workshop on Monolingual Machine Translation
Tsuyoshi Okita | Artem Sokolov | Taro Watanabe
Workshop on Monolingual Machine Translation

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Annotated Corpora for Word Alignment between Japanese and English and its Evaluation with MAP-based Word Aligner
Tsuyoshi Okita
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents two annotated corpora for word alignment between Japanese and English. We annotated on top of the IWSLT-2006 and the NTCIR-8 corpora. The IWSLT-2006 corpus is in the domain of travel conversation while the NTCIR-8 corpus is in the domain of patent. We annotated the first 500 sentence pairs from the IWSLT-2006 corpus and the first 100 sentence pairs from the NTCIR-8 corpus. After mentioned the annotation guideline, we present two evaluation algorithms how to use such hand-annotated corpora: although one is a well-known algorithm for word alignment researchers, one is novel which intends to evaluate a MAP-based word aligner of Okita et al. (2010b).

2010

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Multi-Word Expression-Sensitive Word Alignment
Tsuyoshi Okita | Alfredo Maldonado Guerra | Yvette Graham | Andy Way
Proceedings of the 4th Workshop on Cross Lingual Information Access

2009

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Low-resource machine translation using MaTrEx
Yanjun Ma | Tsuyoshi Okita | Özlem Çetinoğlu | Jinhua Du | Andy Way
Proceedings of the 6th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we give a description of the Machine Translation (MT) system developed at DCU that was used for our fourth participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2009). Two techniques are deployed in our system in order to improve the translation quality in a low-resource scenario. The first technique is to use multiple segmentations in MT training and to utilise word lattices in decoding stage. The second technique is used to select the optimal training data that can be used to build MT systems. In this year’s participation, we use three different prototype SMT systems, and the output from each system are combined using standard system combination method. Our system is the top system for Chinese–English CHALLENGE task in terms of BLEU score.

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Data Cleaning for Word Alignment
Tsuyoshi Okita
Proceedings of the ACL-IJCNLP 2009 Student Research Workshop