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.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Majid Razmara, George Foster, Baskaran Sankaran, Anoop Sarkar
• Keywords: ACL 2012, Computational Linguistics, Mixture Models, Statistical Machine Translation, Translation Model