Sciweavers

Share
IALP
2010

Sentence Similarity-Based Source Context Modelling in PBSMT

8 years 2 months ago
Sentence Similarity-Based Source Context Modelling in PBSMT
Target phrase selection, a crucial component of the state-of-the-art phrase-based statistical machine translation (PBSMT) model, plays a key role in generating accurate translation hypotheses. Inspired by context-rich word-sense disambiguation techniques, machine translation (MT) researchers have successfully integrated various types of source language context into the PBSMT model to improve target phrase selection. Among the various types of lexical and syntactic features, lexical syntactic descriptions in the form of supertags that preserve long-range word-to-word dependencies in a sentence have proven to be effective. These rich contextual features are able to disambiguate a source phrase, on the basis of the local syntactic behaviour of that phrase. In addition to local contextual information, global contextual information such as the grammatical structure of a sentence, sentence length and n-gram word sequences could provide additional important information to enhance this phrase-...
Rejwanul Haque, Sudip Kumar Naskar, Andy Way, Mart
Added 17 May 2011
Updated 17 May 2011
Type Journal
Year 2010
Where IALP
Authors Rejwanul Haque, Sudip Kumar Naskar, Andy Way, Marta R. Costa-Jussà, Rafael E. Banchs
Comments (0)
books