Can Machine Learning Algorithms Improve Phrase Selection in Hybrid Machine Translation

Abstract We describe a substitution-based, hybrid machine translation (MT) system that has been extended with a machine learning component controlling its phrase selection. Our approach is based on a rule-based MT (RBMT) system which creates template translations. Based on the generation parse tree of the RBMT system and standard word alignment computation, we identify potential "translation snippets" from one or more translation engines which could be substituted into our translation templates. The substitution process is controlled by a binary classifier trained on feature vectors from the different MT engines. Using a set of manually annotated training data, we are able to observe improvements in terms of BLEU scores over a baseline version of the hybrid system