Abstract
We propose a phrase-based context-dependent joint probability model for Named Entity (NE) translation. Our proposed model consists of a lexical mapping model and a permutation model. Target phrases are generated by the context-dependent lexical mapping model, and word reordering is performed by the permutation model at the phrase level. We also present a two-step search to decode the best result from the models. Our proposed model is evaluated on the LDC Chinese-English NE translation corpus. The experiment results show that our proposed model is high effective for NE translation.
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