A Large-scale Study of Statistical Machine Translation Methods for Khmer Language.

This paper contributes the first published evaluation of the quality of automatic translation between Khmer (the official language of Cambodia) and twenty other languages, in both directions. The experiments were carried out using three different statistical machine translation approaches: phrase-based, hierarchical phrase-based, and the operation sequence model (OSM). In addition two different segmentation schemes for Khmer were studied, these were syllable segmentation and supervised word segmentation. The results show that the highest quality machine translation was attained with word segmentation in all of the experiments. Furthermore, with the exception of very distant language pairs the OSM approach gave the highest quality translations when measured in terms of both the BLEU and RIBES scores. For distant languages, our results showed a hierarchical phrase-based approach to be the most effective. An analysis of the experimental results indicated that Kendall’s tau may be directly used as a means of selecting an appropriate machine translation approach for a given language pair.