| Author | Search for: Razmara, Majid; Search for: Foster, George1; Search for: Sankaran, Baskaran; Search for: Sarkar, Anoop |
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| Affiliation | - National Research Council of Canada
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| Format | Text, Article |
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| Conference | 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju Island, Republic of Korea, 8-14 July, 2012 |
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| Abstract | Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain. We propose a novel approach, ensemble decoding, which combines a number of translation systems dynamically at the decoding step. In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain. Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation. |
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| Publication date | 2012-07 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 20494947 |
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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 | 3a79c192-af06-4ff4-84a8-fbc03003c772 |
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| Record created | 2012-08-16 |
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| Record modified | 2020-04-21 |
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