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-...

• Type: Journal
• Year: 2010
• Where: IALP
• Authors: Rejwanul Haque, Sudip Kumar Naskar, Andy Way, Marta R. Costa-Jussà, Rafael E. Banchs
• Keywords: Emerging Technology, IALP 2010, Machine Translation, Target Phrase, Various Types