Example-based machine translation based on tree–string correspondence and statistical generation |
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Authors: | Zhanyi Liu Haifeng Wang Hua Wu |
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Affiliation: | (1) Toshiba (China) Research and Development Center, 501, Tower W2, Oriental Plaza, No.1, East Chang An Ave., Dong Cheng District, Beijing, 100738, China |
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Abstract: | This paper describes an example-based machine translation (EBMT) method based on tree–string correspondence (TSC) and statistical
generation. In this method, the translation example is represented as a TSC, which is a triple consisting of a parse tree
in the source language, a string in the target language, and the correspondence between the leaf node of the source-language
tree and the substring of the target-language string. For an input sentence to be translated, it is first parsed into a tree.
Then the TSC forest which best matches the input tree is searched for. Finally the translation is generated using a statistical
generation model to combine the target-language strings of the TSCs. The generation model consists of three features: the
semantic similarity between the tree in the TSC and the input tree, the translation probability of translating the source
word into the target word, and the language-model probability for the target-language string. Based on the above method, we
build an English-to-Chinese MT system. Experimental results indicate that the performance of our system is comparable with
phrase-based statistical MT systems. |
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Keywords: | Example-based machine translation Translation example Tree– string correspondence Statistical generation |
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