Exploiting Morphology and Local Word Reordering in English-to-Turkish Phrase-Based Statistical Machine Translation

In this paper, we present the results of our work on the development of a phrase-based statistical machine translation prototype from English to Turkish—an agglutinative language with very productive inflectional and derivational morphology. We experiment with different morpheme-level representations for English–Turkish parallel texts. Additionally, to help with word alignment, we experiment with local word reordering on the English side, to bring the word order of specific English prepositional phrases and auxiliary verb complexes, in line with the morpheme order of the corresponding case-marked nouns and complex verbs, on the Turkish side. To alleviate the dearth of the parallel data available, we also augment the training data with sentences just with content word roots obtained from the original training data to bias root word alignment, and with highly reliable phrase-pairs from an earlier corpus alignment. We use a morpheme-based language model in decoding and a word-based language model in re-ranking the
$n$
-best lists generated by the decoder. Lastly, we present a scheme for
repairing
the decoder output by
correcting
words which have incorrect morphological structure or which are out-of-vocabulary with respect to the training data and language model, to further improve the translations. We improve from 15.53 BLEU points for our word-based baseline model to 25.17 BLEU points for an improvement of 9.64 points or about 62% relative.