A Study of Statistical Machine Translation Methods for Under Resourced Languages

Abstract

This paper contributes an empirical study of the application of five state-of-the-art machine translation to the trans- lation of low-resource languages. The methods studied were phrase-based, hierarchical phrase-based, the operational sequence model, string-to-tree, tree-to-string statistical machine translation methods between English (en) and the under resourced languages Lao (la), Myanmar (mm), Thai (th) in both directions. The performance of the machine translation systems was automatically measured in terms of BLEU and RIBES for all experiments. Our main findings were that the phrase-based SMT method generally gave the highest BLEU scores. This was counter to expectations, and we believe indicates that this method may be more robust to limitations on the data set size. However, when evaluated with RIBES, the best scores came from methods other than phrase-based SMT, indicating that the other methods were able to handle the word re-ordering better even under the constraint of limited data. Our study achieved the highest reported results on the data sets for all translation language pairs.