A Japanese-English Hierarchical Phrase-based Translation Model Integrating Tense Features

doi:10.3969/j.issn.1006-2475.2017.06.001

Abstract

Abstract: In view of the problem that limited contextual information is used in the hierarchical phrase-based (HPB) translation model and the quality of tense translation is not high, this paper proposes a method to integrate tense features into Japanese-English HPB translation. Our method adopts the information of tense as constraints for tense classification model construction, and integrates tense features into HPB translation model, the decoder can get the best-matching rules according to the results of potential tense classification of rules. Firstly, we extract training data from bilingual training corpus to train tense classification models by using maximum entropy. Secondly, we extract tense features from hierarchy phrase rules to classify each kind of rules which include tense information, then we take the tense classification results as a kind of new translation features, and integrate the features into hierarchy phrase-based translation model. The experimental results show that our method can achieve good performance in Japanese-English HPB translation.

Key words: hierarchical phrase-based translation model, tense features, maximum entropy classification model

CLC Number:

TP391

MING Fang, XU Jin-an, WANG Nan, CHEN Yu-feng, ZHANG Yu-jie. A Japanese-English Hierarchical Phrase-based Translation Model Integrating Tense Features[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.06.001.

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