| Download | - View accepted manuscript: Semi-supervised model adaptation for statistical machine translation (PDF, 604 KiB)
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| Author | Search for: Ueffing, Nicola; Search for: Haffari, G.; Search for: Sarkar, A. |
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| Format | Text, Article |
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| Subject | statistical machine translation; self-training; semi-supervised learning; domain adaptation; model adaptation; auto-apprentissage; apprentissage semi-supervisé; adaptation au domaine; adaptation de modèle |
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| Abstract | Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French-English EuroParl data set and on data from the NIST Chinese-English large-data track. We show a significant improvement in translation quality on both tasks. |
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| Publication date | 2008 |
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| In | |
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| Language | English |
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| NRC number | NRCC 50408 |
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| NPARC number | 5765611 |
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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 | 39812cd2-dbe9-4352-9b66-0f484e259af0 |
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| Record created | 2009-03-29 |
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| Record modified | 2020-04-15 |
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