Supertagged phrase-based statistical machine translation

Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic structure caused system per-formance to deteriorate. In this work we show that incorporating lexical syntactic de-scriptions in the form of supertags can yield significantly better PBSMT systems. We de-scribe a novel PBSMT model that integrates supertags into the target language model and the target side of the translation model. Two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. De-spite the differences between these two ap-proaches, the supertaggers give similar im-provements. In addition to supertagging, we also explore the utility of a surface global grammaticality measure based on combina-tory operators. We perform various experi-ments on the Arabic to English NIST 2005 test set addressing issues such as sparseness, scalability and the utility of system subcom-ponents. Our best result (0.4688 BLEU) improves by 6.1 % relative to a state-of-the-art PBSMT model, which compares very favourably with the leading systems on the NIST 2005 task