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Mixing multiple translation models in statistical machine translation

 
 
Affiliation:
National Research Council Canada
Language:
English
Type:
Conference publication
Conference:
50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju Island, Republic of Korea, 8-14 July, 2012
Proceedings
Title:
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics
Date:
2012
NPArC #:
20494947
Program(s):
Natural Language Processing; Traitement des langues naturelles
Group(s):
Interactive Language Technologies; Technologies langagières interactives
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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