Minimum Error Rate Training Based on Ensemble Learning
Fan Chen · Journal of Xiamen University · 2015
Minimum error rate training(MERT)is a standard tuning parameter procedure in statistical machine translation,playing a significant role in the process.However,the overfitting phenomenon is likely to occur in the original MERT.In other words,weights trained from development set cannot be fit for test sets.In view of this issue,we adopt ensemble learning method to the training process in this paper.To be specific,we first select different feature subsets to acquire several groups of feature weights through MERT,and then filter out unreasonable weights according to their spatial distance,and at last we compute the weighted average as the final feature weight based on their BLEU scores on development set.Experiments on NIST and IWSLT show that our method is efficient for the translation tasks using the training and testing data sets of different domains.