Machine Translation with Significant Word Reordering and Rich Target-Side Morphology

This paper describes the integration of morpho-syntactic information in phrase-based and syntax-based Machine Translation systems. We mainly focus on translating in the hard direction which is translating from morphologically poor to morphologically richer languages and also between language pairs that have significant word order differences. We intend to use hierarchical or surface syntactic models for languages of large vocabulary size and improve the translation quality using two-step approach [Fraser, 2009]. The two-step scheme basically reduces the complexity of hypothesis construction and selection by separating the task of source-to-target reordering from the task of generating fully inflected target-side word forms. In the first step, reordering is performed on the source data to make it structurally similar to the target language and in the second step, lemmatized target words are mapped to fully inflected target words. We will first introduce the reader to the detailed architecture of the two-step translation setup and later its further proposed enhancements for dealing with the above mentioned issues. We plan to conduct experiments for two language pairs: English-Urdu and English-Czech.