Mitsuo Shimohata

Also published as: M. Shimohata


2005

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Acquiring Synonyms from Monolingual Comparable Texts
Mitsuo Shimohata | Eiichiro Sumita
Second International Joint Conference on Natural Language Processing: Full Papers

2004

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Building a Paraphrase Corpus for Speech Translation
Mitsuo Shimohata | Eiichiro Sumita | Yuji Matsumoto
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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EBMT, SMT, hybrid and more: ATR spoken language translation system
Eiichiro Sumita | Yasuhiro Akiba | Takao Doi | Andrew Finch | Kenji Imamura | Hideo Okuma | Michael Paul | Mitsuo Shimohata | Taro Watanabe
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

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Method for retrieving a similar sentence and its application to machine translation
Mitsuo Shimohata | Eiichiro Sumita | Yuji Matsumoto
Proceedings of the 10th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

2003

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Retrieving Meaning-equivalent Sentences for Example-based Rough Translation
Mitsuo Shimohata | Eiichiro Sumita | Yuji Matsumoto
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

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A corpus-centered approach to spoken language translation
Eiichiro Sumita | Yasuhiro Akiba | Takao Doi | Andrew Finch | Kenji Imamura | Michael Paul | Mitsuo Shimohata | Taro Watanabe
10th Conference of the European Chapter of the Association for Computational Linguistics

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Example-based rough translation for speech-to-speech translation
Mitsuo Shimohata | Eiichiro Sumita | Yuji Matsumoto
Proceedings of Machine Translation Summit IX: Papers

Example-based machine translation (EBMT) is a promising translation method for speech-to-speech translation (S2ST) because of its robustness. However, it has two problems in that the performance degrades when input sentences are long and when the style of the input sentences and that of the example corpus are different. This paper proposes example-based rough translation to overcome these two problems. The rough translation method relies on “meaning-equivalent sentences,” which share the main meaning with an input sentence despite missing some unimportant information. This method facilitates retrieval of meaning-equivalent sentences for long input sentences. The retrieval of meaning-equivalent sentences is based on content words, modality, and tense. This method also provides robustness against the style differences between the input sentence and the example corpus.

2002

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Automatic paraphrasing based on parallel corpus for normalization
Mitsuo Shimohata | Eiichiro Sumita
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Statistical Machine Translation on Paraphrased Corpora
Taro Watanabe | Mitsuo Shimohata | Eiichiro Sumita
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Identifying Synonymous Expressions from a Bilingual Corpus for Example-Based Machine Translation
Mitsuo Shimohata | Eiichiro Sumita
COLING-02: Machine Translation in Asia

1998

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Description of the Oki System as Used for MET-2
J. Fukumoto | M. Shimohata | F. Masui | M. Saski
Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998