Extending Hiero Decoding in Moses with Cube Growing

Hierarchical phrase-based (Hiero) models have richer expressiveness than phrase-based models and have shown promising translation quality gains for many language pairs whose syntactic divergences, such as reordering, could be better captured. However, their expressiveness comes at a high computational cost in decoding, which is induced by huge dynamic programs associated with language model integrated decoding, where the search space is lexically exploded and exact search often becomes intractable. Cube pruning and growing are two approximate search algorithms to make decoding much more efficient. In this article, we describe an extension to the Hiero decoder of the Moses toolkit by providing cube growing as an alternative to cube pruning, with an additional parameter similar to Jane’s cube growing implementation that is not present in the original one. We also report experimental results on a full-scale NIST MT08 Chinese-English translation task.