Softmax-margin training for statistical machine translation

The training procedure is very important in statistical machine translation (SMT). It has a great influence on the final performance of a translation system. The widely used method in SMT is the minimum error rate training (MERT). It is effective to estimate the feature function weights. However, MERT does not use regularization and has been observed to over-fit. In this paper, we describe a method named softmax-margin, which is a modification of the max-margin training. This approach is simple, efficient, and easy to implement. We conduct our work using data sets from the WMT shared tasks. The results of experiment on small scale French-English translation task reach a competitive performance compared to MERT.