Improving English-to-Chinese translation for technical terms using morphological information

The continuous emergence of new technical terms and the difficulty of keeping up with neologism in parallel corpora deteriorate the performance of statistical machine translation (SMT) systems. This paper explores the use of morphological information to improve English-to-Chinese translation for technical terms. To reduce the morpheme-level translation ambiguity, we group the morphemes into morpheme phrases and propose the use of domain information for translation candidate selection. In order to find correspondences of morpheme phrases between the source and target languages, we propose an algorithm to mine morpheme phrase translation pairs from a bilingual lexicon. We also build a cascaded translation model that dynamically shifts translation units from phrase level to word and morpheme phrase levels. The experimental results show the significant improvements over the current phrase-based SMT systems.