@inproceedings{liu-etal-2005-example,
title = "Example-based Machine Translation Based on {TSC} and Statistical Generation",
author = "Liu, Zhanyi and
Wang, Haifeng and
Wu, Hua",
booktitle = "Proceedings of Machine Translation Summit X: Papers",
month = sep # " 13-15",
year = "2005",
address = "Phuket, Thailand",
url = "https://aclanthology.org/2005.mtsummit-papers.4",
pages = "25--32",
abstract = "This paper proposes a novel Example-Based Machine Translation (EBMT) method based on Tree String Correspondence (TSC) and statistical generation. In this method, the translation examples are represented as TSC, which consists of three parts: a parse tree in the source language, a string in the target language, and the correspondences between the leaf nodes of the source language tree and the substrings of the target language string. During the translation, the input sentence is first parsed into a tree. Then the TSC forest is searched out if it is best matched with the parse tree. The translation is generated by using a statistical generation model to combine the target language strings in the TSCs. The generation model consists of three parts: the semantic similarity between words, the word translation probability, and the target language model. Based on the above method, we build an English-to-Chinese Machine Translation (ECMT) system. Experimental results indicate that the performance of our system is comparable with that of the state-of-the-art commercial ECMT systems.",
}
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<abstract>This paper proposes a novel Example-Based Machine Translation (EBMT) method based on Tree String Correspondence (TSC) and statistical generation. In this method, the translation examples are represented as TSC, which consists of three parts: a parse tree in the source language, a string in the target language, and the correspondences between the leaf nodes of the source language tree and the substrings of the target language string. During the translation, the input sentence is first parsed into a tree. Then the TSC forest is searched out if it is best matched with the parse tree. The translation is generated by using a statistical generation model to combine the target language strings in the TSCs. The generation model consists of three parts: the semantic similarity between words, the word translation probability, and the target language model. Based on the above method, we build an English-to-Chinese Machine Translation (ECMT) system. Experimental results indicate that the performance of our system is comparable with that of the state-of-the-art commercial ECMT systems.</abstract>
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%0 Conference Proceedings
%T Example-based Machine Translation Based on TSC and Statistical Generation
%A Liu, Zhanyi
%A Wang, Haifeng
%A Wu, Hua
%S Proceedings of Machine Translation Summit X: Papers
%D 2005
%8 sep 13 15
%C Phuket, Thailand
%F liu-etal-2005-example
%X This paper proposes a novel Example-Based Machine Translation (EBMT) method based on Tree String Correspondence (TSC) and statistical generation. In this method, the translation examples are represented as TSC, which consists of three parts: a parse tree in the source language, a string in the target language, and the correspondences between the leaf nodes of the source language tree and the substrings of the target language string. During the translation, the input sentence is first parsed into a tree. Then the TSC forest is searched out if it is best matched with the parse tree. The translation is generated by using a statistical generation model to combine the target language strings in the TSCs. The generation model consists of three parts: the semantic similarity between words, the word translation probability, and the target language model. Based on the above method, we build an English-to-Chinese Machine Translation (ECMT) system. Experimental results indicate that the performance of our system is comparable with that of the state-of-the-art commercial ECMT systems.
%U https://aclanthology.org/2005.mtsummit-papers.4
%P 25-32
Markdown (Informal)
[Example-based Machine Translation Based on TSC and Statistical Generation](https://aclanthology.org/2005.mtsummit-papers.4) (Liu et al., MTSummit 2005)
ACL