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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haque, R. Naskar, S.K. Way, A. Costa-jussa, M.R. Banchs, R.E. |
| Copyright Year | 2010 |
| Abstract | 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 super tags 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-sense disambiguation. In this work, we explore various sentence similarity features by measuring similarity between a source sentence to be translated with the source-side of the bilingual training sentences and integrate them directly into the PBSMT model. We performed experiments on an English-to-Chinese translation task by applying sentence-similarity features both individually, and collaboratively with super tag-based features. We evaluate the performance of our approach and report a statistically significant relative improvement of 5.25% BLEU score when adding a sentence-similarity feature together with a super tag-based feature. |
| Starting Page | 257 |
| Ending Page | 260 |
| File Size | 304152 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424490639 |
| DOI | 10.1109/IALP.2010.45 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Context Computational modeling Statistical machine translation Syntactics Feature extraction Grammar Sentence similarity Source context information Context modeling |
| Content Type | Text |
| Resource Type | Article |
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