Parse and Corpus-Based Machine Translation

The current state-of-the-art in machine translation consists of phrase-based statistical machine translation (PB-SMT) [23], an approach which has been used since the late 1990s, evolving from word-based SMT proposed by IBM [5]. These stringbased techniques (which use no linguistic knowledge) seem to have reached their ceiling in terms of translation quality, while there are still a number of limitations to the model. It lacks a mechanism to deal with long-distance dependencies, it has no means to generalise over non-overt linguistic information [37] and it has limited word reordering capabilities. Furthermore, in some cases the output quality may lack appropriate fluency and grammaticality to be acceptable for actual MT users. Sometimes essential words are missing from the translation. To overcome these limitations efforts have been made to introduce syntactic knowledge into the statistical paradigm, usually in the form of syntax trees, either