A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Automated generalization of translation examples
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
CTM: an example-based translation aid system
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 4
An efficient statistical speech act type tagging system for speech translation systems
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Low-cost, high-performance translation retrieval: dumber is better
ACL '01 Proceedings of the 39th 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
The VI framework program in Europe: some thoughts about speech to speech translation research
S2S '02 Proceedings of the ACL-02 workshop on Speech-to-speech translation: algorithms and systems - Volume 7
High-quality speech-to-speech translation for computer-aided language learning
ACM Transactions on Speech and Language Processing (TSLP)
Language resources for the Semantic Web: perspectives for machine translation
LRTWRT '04 Proceedings of the Second International Workshop on Language Resources for Translation Work, Research and Training
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Example-based machine translation (EBMT) is a promising translation method for speech-to-speech translation because of its robustness. It retrieves example sentences similar to the input and adjusts their translations to obtain the output. However, it has problems in that the performance degrades when input sentences are long and when the style of inputs and that of the example corpus are different. This paper proposes a method for retrieving "meaning-equivalent sentences" to overcome these two problems. A meaning-equivalent sentence shares the main meaning with an input despite lacking some unimportant information. The translations of meaning-equivalent sentences correspond to "rough translations." The retrieval is based on content words, modality, and tense.