Statistical machine translation using hierarchical phrase alignment

The following three problems are known to exist with statistical machine translation. (1) the modeling problem involved in prescribing translation relations, (2) the problem of determining parameter settings from a text corpus of translations, and (3) the search problem involved in determining the output text (the translation) given a statistical model and an input text. In this paper we find alignments of translations using phrase-based units in a hierarchical fashion with the intention of solving the above-mentioned modeling and training problems with such hierarchical phrase alignments. As an initial method we perform chunking on the corpus on the basis of these hierarchical alignments, and create translation models using these chunks as translation units. Then, as a second method we convert the translation relations expressed in the hierarchical phrase alignments into correspondences in the translation model, and perform additional training having initialized the model parameters to values obtained from these relations. The results of experiments with Japanese-to-English translation show that both methods improve performance with the second method being particularly effective resulting in an increase in translation rate from 61.3% to 70.0%.