A Performance Study of Cube Pruning for Large-Scale Hierarchical Machine Translation
Matthias Huck, David Vilar, Markus Freitag, Hermann Ney · 2013
In this paper, we empirically investigate the impact of critical configuration parameters in the popular cube pruning algorithm for decoding in hierarchical statistical machine translation. Specifically, we study how the choice of the k-best generation size affects translation quality and resource requirements in hierarchical search. We furthermore examine the influence of two different granularities of hypothesis recombination. Our experiments are conducted on the large-scale Chinese→English and Arabic→English NIST translation tasks. Besides standard hierarchical grammars, we also explore search with restricted recursion depth of hierarchical rules based on shallow-1 grammars. 1