Paper: Mixing Multiple Translation Models in Statistical Machine Translation

ACL ID P12-1099
Title Mixing Multiple Translation Models in Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single trans- lation model which then has to translate sen- tences in a new domain. We propose a novel approach, ensemble decoding, which com- bines a number of translation systems dynam- ically at the decoding step. In this paper, we evaluate performance on a domain adap- tation setting where we translate sentences from the medical domain. Our experimental results show that ensemble decoding outper- forms various strong baselines including mix- ture models, the current state-of-the-art for do- main adaptation in machine translation.