acl acl2012 acl2012-143 acl2012-143-reference knowledge-graph by maker-knowledge-mining
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Author: Majid Razmara ; George Foster ; Baskaran Sankaran ; Anoop Sarkar
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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