Paper: Supertagged Phrase-Based Statistical Machine Translation

ACL ID P07-1037
Title Supertagged Phrase-Based Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

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 perfo...