Setsuo Yamada


2012

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Bangla Phonetic Input Method with Foreign Words Handling
Khan Md. Anwarus Salam | Setsuo Yamada | Tetsuro Nishino
Proceedings of the Second Workshop on Advances in Text Input Methods

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Sublexical Translations for Low-Resource Language
Khan Md. Anwarus Salam | Setsuo Yamada | Tetsuro Nishino
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages

2011

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Example-Based Machine Translation for Low-Resource Language Using Chunk-String Templates
Md. Anwarus Salam Khan | Setsuo Yamada | Tetsuro Nishino
Proceedings of Machine Translation Summit XIII: Papers

2006

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Searching for Sentences Expressing Opinions by using Declaratively Subjective Clues
Nobuaki Hiroshima | Setsuo Yamada | Osamu Furuse | Ryoji Kataoka
Proceedings of the Workshop on Sentiment and Subjectivity in Text

2005

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A Report on the Machine Translation Market in Japan
Setsuo Yamada | Syuuji Kodama | Taeko Matsuoka | Hiroshi Araki | Yoshiaki Murakami | Osamu Takano | Yoshiyuki Sakamoto
Proceedings of Machine Translation Summit X: Papers

When conducting market research on machine translation, we research the volume of sales continuously in order to determine the scale of the machine translation market in Japan. We have officially announced these figures every year. Furthermore, since 2003, we administered questionnaires regarding the Web translation.

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Building a conversation corpus by text derivation from “germ dialogs”
Naoki Asanoma | Setsuo Yamada | Osamu Furuse | Masahiro Oku
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

2003

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Improving translation models by applying asymmetric learning
Setsuo Yamada | Masaaki Nagata | Kenji Yamada
Proceedings of Machine Translation Summit IX: Papers

The statistical Machine Translation Model has two components: a language model and a translation model. This paper describes how to improve the quality of the translation model by using the common word pairs extracted by two asymmetric learning approaches. One set of word pairs is extracted by Viterbi alignment using a translation model, the other set is extracted by Viterbi alignment using another translation model created by reversing the languages. The common word pairs are extracted as the same word pairs in the two sets of word pairs. We conducted experiments using English and Japanese. Our method improves the quality of a original translation model by 5.7%. The experiments also show that the proposed learning method improves the word alignment quality independent of the training domain and the translation model. Moreover, we show that common word pairs are almost as useful as regular dictionary entries for training purposes.

2002

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Corpus-assisted expansion of manual MT knowledge:
Setsuo Yamada | Kenji Imamura | Kazuhide Yamamoto
Proceedings of the 9th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Papers

2001

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ALT-J/C: a prototype Japanese-to-Chinese automatic language translation system
Minoru Hayashi | Setsuo Yamada | Akira Kataoka | Akio Yokoo
Proceedings of Machine Translation Summit VIII

This paper describes a prototype Japanese-to-Chinese automatic language translation system. ALT-J/C (Automatic Language Translator - Japanese-to-Chinese) is a semantic transfer based system, which is based on ALT-J/E (a Japanese-to-English system), but written to cope with Unicode. It is also designed to cope with constructions specific to Chinese. This system has the potential to become a framework for multilingual translation systems.

2000

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Translation using Information on Dialogue Participants
Setsuo Yamada | Eiichiro Sumita | Hideki Kashioka
Sixth Applied Natural Language Processing Conference

1999

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Solutions to problems inherent in spoken-language translation: the ATR-MATRIX approach
Eiichiro Sumita | Setsuo Yamada | Kazuhide Yamamoto | Michael Paul | Hideki Kashioka | Kai Ishikawa | Satoshi Shirai
Proceedings of Machine Translation Summit VII

ATR has built a multi-language speech translation system called ATR-MATRIX. It consists of a spoken-language translation subsystem, which is the focus of this paper, together with a highly accurate speech recognition subsystem and a high-definition speech synthesis subsystem. This paper gives a road map of solutions to the problems inherent in spoken-language translation. Spoken-language translation systems need to tackle difficult problems such as ungrammaticality. contextual phenomena, speech recognition errors, and the high-speeds required for real-time use. We have made great strides towards solving these problems in recent years. Our approach mainly uses an example-based translation model called TDMT. We have added the use of extra-linguistic information, a decision tree learning mechanism, and methods dealing with recognition errors.

1998

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Splitting Long or Ill-formed Input for Robust Spoken-language Translation
Osamu Furuse | Setsuo Yamada | Kazuhide Yamamoto
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Splitting Long or Ill-formed Input for Robust Spoken-language Translation
Osamu Furuse | Setsuo Yamada | Kazuhide Yamamoto
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

1995

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A Method for Automatically Adapting an MT System to Different Domains
Setsuo Yamada | Hiromi Nakaiwa | Kentaro Ogura | Satoru Ikehara
Proceedings of the Sixth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages