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2012

Mixing Multiple Translation Models in Statistical Machine Translation

7 years 2 months ago
Mixing Multiple Translation Models in Statistical Machine Translation
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.
Majid Razmara, George Foster, Baskaran Sankaran, A
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Majid Razmara, George Foster, Baskaran Sankaran, Anoop Sarkar
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