TY  - CONF
ID  - 20494947
AU  - Razmara, Majid
AU  - Foster, George
AU  - Sankaran, Baskaran
AU  - Sarkar, Anoop
T1  - Mixing multiple translation models in statistical machine translation
C8  - Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics
T3  - 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju Island, Republic of Korea, 8-14 July, 2012
PB  - Association for Computational Linguistics
PY  - 2012
N2  - 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.
U3  - National Research Council Canada
U3  - Conseil national de recherches Canada
ER  -