Translation Model Generalization using Probability Averaging for Machine Translation
Nan Duan, Hong Min Sun, Ming Zhou · 2010
Previous methods on improving transla-tion quality by employing multiple SMT models usually carry out as a second-pass decision procedure on hypotheses from multiple systems using extra fea-tures instead of using features in existing models in more depth. In this paper, we propose translation model generalization (TMG), an approach that updates proba-bility feature values for the translation model being used based on the model it-self and a set of auxiliary models, aiming to enhance translation quality in the first-pass decoding. We validate our approach on translation models based on auxiliary models built by two different ways. We also introduce novel probability variance features into the log-linear models for further improvements. We conclude that our approach can be developed indepen-dently and integrated into current SMT pipeline directly. We demonstrate BLEU improvements on the NIST Chinese-to-English MT tasks for single-system de-codings, a system combination approach and a model combination approach. 1