• URL

Razmara, M., Foster, G., Sankaran, B., & Sarkar, A. (2012). Mixing Multiple Translation Models in Statistical Machine Translation. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju, Republic of Korea: Association for Computational Linguistics.

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.

@inproceedings{Razmara:2012:ACL,
  author = {Razmara, Majid and Foster, George and Sankaran, Baskaran and Sarkar, Anoop},
  title = {Mixing Multiple Translation Models in Statistical Machine Translation},
  booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics},
  month = {jul},
  year = {2012},
  address = {Jeju, Republic of Korea},
  publisher = {Association for Computational Linguistics},
  url = {http://www.aclweb.org/anthology/P12-1099}
}