Transductive Minimum Error Rate Training for Statistical Machine Translation
Yinggong Zhao, Shujie Liu, Yangsheng Ji, Jiajun Chen, Guodong Zhou · 2011
This paper investigates parameter adap-tation in Statistical Machine Transla-tion(SMT). To overcome the parameter bias-estimation problem with Minimum Error Rate Training(MERT), we extend it under a transductive learning framework, by iteratively re-estimating the parame-ters using both development and test da-ta, in which the translation hypotheses of the test data are used as pseudo ref-erences. Furthermore, in order to over-come the over-training and unstableness problems respectively in employing such pseudo references, a termination criterion using a hyper-parameter and a Minimum Bayes Risk(MBR)-based hypothesis se-lection method are proposed in our work. Experimental results show that the trans-ductive MERT method could yield signif-icant performance improvements over a strong baseline on a large-scale Chinese-to-English translation task. 1