Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Simultaneous interpretation utilizing example-based incremental transfer
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Architectures for speech-to-speech translation using finite-state models
S2S '02 Proceedings of the ACL-02 workshop on Speech-to-speech translation: algorithms and systems - Volume 7
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This paper proposes a method for incrementally translating English spoken language into Japanese. To realize simultaneous translation between languages with different word order, such as English and Japanese, our method utilizes the feature that the word order of a target language is flexible. To resolve the problem of generating a grammatically incorrect sentence, our method uses dependency structures and Japanese dependency constraints to determine the word order of a translation. Moreover, by considering the fact that the inversion of predicate expressions occurs more frequently in Japanese spoken language, our method takes advantage of a predicate inversion to resolve the problem that Japanese has the predicate at the end of a sentence. Furthermore, our method includes the function of canceling an inversion by restating a predicate when the translation is incomprehensible due to the inversion. We implement a prototype translation system and conduct an experiment with all 578 sentences in the ATIS corpus. The results indicate improvements in comparison to two other methods.