A Statistical Method for Translating Chinese into Under-resourced Minority Languages

In order to improve the performance of statistical machine translation between Chinese and minority languages, most of which are under-resourced languages with different word order and rich morphology, the paper proposes a method which incorporates syntactic information of the source-side and morphological information of the target-side to simultaneously reduce the differences of word order and morphology. First, according to the word alignment and the phrase structure trees of source language, reordering rules are extracted automatically to adjust the word order at source side. And then based on Hidden Markov Model, a morphological segmentation method is adopted to obtain morphological information of the target language. In the experiments, we take the Chinese-Mongolian translation as an example. A morpheme-level statistical machine translation system, constructed based on the reordered source side and the segmented target side, achieves 2.1 BLEU points increment over the standard phrase-based system.

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