A Maximum Entropy Approach to Syntactic Translation Rule Filtering

In this paper we will present a maximum entropy filter for the translation rules of a statistical machine translation system based on tree transducers. This filter can be successfully used to reduce the number of translation rules by more than 70% without negatively affecting translation quality as measured by BLEU. For some filter configurations, translation quality is even improved.

Our investigations include a discussion of the relationship of Alignment Error Rate and Consistent Translation Rule Score with translation quality in the context of Syntactic Statistical Machine Translation.