A Semantic-Specific Model for Chinese Named Entity Translation

We observe that (1) it is difficult to combine transliteration and meaning translation when transforming named entities (NE); and (2) there are different translation variations in NE translation, due to different semantic information. From this basis, we propose a novel semantic-specific NE translation model, which automatically incorporates the global context from corpus in order to capture substantial semantic information. The presented approach is inspired by example-based translation and realized by log-linear models, integrating monolingual context similarity model, bilingual context similarity model, and mixed language model. The experiments show that the semantic-specific model has substantially and consistently outperformed the baselines and related NE translation systems.