Kai Ishikawa


2013

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Translation Acquisition Using Synonym Sets
Daniel Andrade | Masaaki Tsuchida | Takashi Onishi | Kai Ishikawa
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Chinese Informal Word Normalization: an Experimental Study
Aobo Wang | Min-Yen Kan | Daniel Andrade | Takashi Onishi | Kai Ishikawa
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Synonym Acquisition Using Bilingual Comparable Corpora
Daniel Andrade | Masaaki Tsuchida | Takashi Onishi | Kai Ishikawa
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2011

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Extractive Summarization Method for Contact Center Dialogues based on Call Logs
Akihiro Tamura | Kai Ishikawa | Masahiro Saikou | Masaaki Tsuchida
Proceedings of 5th International Joint Conference on Natural Language Processing

2006

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Chinese Speech Information Retrieval for Questions on Mobile Phone Operation
Kai Ishikawa | Susumu Akamine | Ken Hanazawa
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

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.