In example-based Machine Translation (EBMT),

This paper describes log-linear generation models for Example-based Machine Translation (EBMT). In the generation model, various knowledge sources are described as the feature functions and are incorporated into the log-linear models. Six features are used in this paper: matching score and context similarity, to estimate the similarity between the input sentence and the translation example; word translation probability and target language string selection probability, to estimate the reliability of the translation example; language model probability and length selection probability, to estimate the quality of the generated translation. In order to evaluate the performance of the log-linear generation models, we build an English-to-Chinese EBMT system with the proposed generation model. Experimental results show that our EBMT system significantly outperforms both a baseline EBMT system and a phrase-based SMT system