Combining phrase and neural-based machine translation: what worked and did not

Abstract

Phrase-based machine translation assumes that all words are at the same distance and translates them using feature functions that approximate the probability at different levels. On the other hand, neural machine translation infers a word embedding and translates these word vectors using a neural model. At the moment, both approaches co-exist and are being intensively investigated. This paper to the best of our knowledge is the first work that both compares and combines these two systems by: using the phrase-based output to solve unknown words in the neural machine translation output; using the neural alignment in the phrase-based system; comparing how the popular strategy of pre-reordering aspects both systems; and combining both translation outputs. Improvements are achieved in Catalan-to- Spanish and German-to-English.