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COLING
2010

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

8 years 6 months ago
Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation
This paper addresses the problem of dynamic model parameter selection for loglinear 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 dependent development set selection problem. We present two algorithms for automatic development set construction, and evaluated our method on several NIST data sets for the Chinese-English translation 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.
Mu Li, Yinggong Zhao, Dongdong Zhang, Ming Zhou
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where COLING
Authors Mu Li, Yinggong Zhao, Dongdong Zhang, Ming Zhou
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