Auteur | Rechercher : Razmara, Majid; Rechercher : Foster, George1; Rechercher : Sankaran, Baskaran; Rechercher : Sarkar, Anoop |
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Affiliation | - Conseil national de recherches du Canada
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Format | Texte, Article |
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Conférence | 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju Island, Republic of Korea, 8-14 July, 2012 |
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Résumé | 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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Date de publication | 2012-07 |
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Dans | |
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Langue | anglais |
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Publications évaluées par des pairs | Oui |
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Numéro NPARC | 20494947 |
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Exporter la notice | Exporter en format RIS |
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Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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Identificateur de l’enregistrement | 3a79c192-af06-4ff4-84a8-fbc03003c772 |
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Enregistrement créé | 2012-08-16 |
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Enregistrement modifié | 2020-04-21 |
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