An Empirical Study on Improving Hierarchical Phrase-based Translation Using Alignment Features

In this paper, we empirically investigate three new features from word alignments to improve speech-to-speech translation on mobile devices for low-resource languages. The three features include one feature about alignment for boundary words of the target side phrase, one about the balance of terminal words between the source and the target side, and another about the number of unaligned words: We carry out experiments on both directions (E2F and F2E) for Pashto and Dari, two official languages of Afghanistan. By using the proposed alignment features, we can obtain improvements (up to 1% BLEU score) on the test sets for both Pashto and Dari.