Paper: Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation

ACL ID C10-1075
Title Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

This paper addresses the problem of dy- namic model parameter selection for log- linear model based statistical machine translation (SMT) systems. In this work, we propose a principled method for this task by transforming it to a test data de- pendent development set selection prob- lem. We present two algorithms for au- tomatic development set construction, and evaluated our method on several NIST data sets for the Chinese-English trans- lation task. Experimental results show that our method can effectively adapt log-linear model parameters to different test data, and consistently achieves good translation performance compared with conventional methods that use a fixed model parameter setting across different data sets.