Cardinality pruning and language model heuristics for hierarchical phrase-based translation

In this article we present two novel enhancements for the cube pruning and cube growing algorithms, two of the most widely applied methods when using the hierarchical approach to statistical machine translation. Cube pruning is the de facto standard search algorithm for the hierarchical model. We propose to adapt concepts of the source cardinality synchronous search organization as used for standard phrase-based translation to the characteristics of cube pruning. In this way we will be able to improve the performance of the generation process and reduce the average translation time per sentence to approximately one quarter. We will also investigate the cube growing algorithm, a reformulation of cube pruning with on-demand computation. This algorithm depends on a heuristic for the language model, but this issue is barely discussed in the original work. We analyze the behaviour of this heuristic and propose a new one which greatly reduces memory consumption without costs in runtime or translation performance. Results are reported on the German–English Europarl corpus.