| Download | - View final version: Bilingual Sense Similarity for Statistical Machine Translation (PDF, 614 KiB)
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| Author | Search for: Chen, Boxing1; Search for: Foster, George1; Search for: Kuhn, Roland1 |
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| Affiliation | - National Research Council Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | 48th Annual Meeting of the Association for Computational Linguistics, July 11–16, 2010, Uppsala, Sweden |
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| Subject | Information and Communications Technologies |
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| Abstract | This paper proposes new algorithms to compute the sense similarity between two units (words, phrases, rules, etc.) from parallel corpora. The sense similarity scores are computed by using the vector space model. We then apply the algorithms to statistical machine translation by computing the sense similarity between the source and target side of translation rule pairs. Similarity scores are used as additional features of the translation model to improve translation performance. Significant improvements are obtained over a state-of-the-art hierarchical phrase-based machine translation system. |
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| Publication date | 2010 |
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| Access condition | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 15736685 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c5a3a3fe-7d31-4f97-9d07-8dda09a155b9 |
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| Record created | 2010-07-05 |
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| Record modified | 2020-05-27 |
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