A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
A statistical approach to machine translation
Computational Linguistics
Review Article: Example-based Machine Translation
Machine Translation
Learning dependency translation models as collections of finite-state head transducers
Computational Linguistics - Special issue on finite-state methods in NLP
A new quantitative quality measure for machine translation systems
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Using language and translation models to select the best among outputs from multiple MT systems
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Example-based machine translation using DP-matching between word sequences
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
Input sentence splitting and translating
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
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To achieve translation technology that is adequate for speech-to-speech translation (S2S), this paper introduces a new attempt named Corpus-Centered Computation, (abbreviated to C3 and pronounced c-cube). As opposed to conventional approaches adopted by machine translation systems for written language, C3 places corpora at the center of the technology. For example, translation knowledge is extracted from corpora, translation quality is gauged by referring to corpora and the corpora themselves are normalized by paraphrasing or filtering. High-quality translation has been demonstrated in the domain of travel conversation, and the prospects of this approach are promising due to the benefits of synergistic effects.