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Improving English-Vietnamese Word Alignment Using Translation Model

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2 Author(s)
Giang Thanh Nguyen ; Linguistic R&D Dept., Kim Tu Dien Multilingual Data Center, Ho Chi Minh City, Vietnam ; Dien Dinh

Word alignment for a parallel corpus is the connection between the words/phrases in source language and the words/phrases in target language. The alignment result is an important input for many natural language processing applications. In this paper, we propose an approach to improve the English-Vietnamese word alignment result by using the alignment frequency that is presented in the translation model of SMT (Statistical Machine Translation). We also indicate 5 common error types of English-Vietnamese word alignment and propose the heuristic patterns to discover the alignment errors. The experimental results show the improvement compared to the result of GIZA++.

Published in:

Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012 IEEE RIVF International Conference on

Date of Conference:

Feb. 27 2012-March 1 2012