Conditional Random Fields for Machine Translation System Combination

Minimum Error Rate Training (MERT) as an effective parameters learning algorithm is widely applied in machine translation and system combination area. However, there exists an ambiguity problem in respect to the training goal and it is hard for MERT to tackle, that is different parameters may lead to the same minimum error rate in training but greatly different performances in testing. We propose a novel training objective as the unique goal for training towards, namely partial references, and by use of conditional random fields (CRF) to cast the decoding procedure in system combination as a sequence labeling problem. Experiments on Chinese-English translation test sets show that our approach significantly outperforms the MERT-based baselines with less training time.