Flexible and Efficient Hypergraph Interactions for Joint Hierarchical and Forest-to-String Decoding

Machine translation benefits from system combination. We propose flexible interaction of hypergraphs as a novel technique combining different translation models within one decoder. We introduce features controlling the interactions between the two systems and explore three interaction schemes of hiero and forest-to-string models—specification, generalization, and interchange. The experiments are carried out on large training data with strong baselines utilizing rich sets of dense and sparse features. All three schemes significantly improve results of any single system on four testsets. We find that specification—a more constrained scheme that almost entirely uses forest-to-string rules, but optionally uses hiero rules for shorter spans—comes out as the strongest, yielding improvement up to 0.9 (Ter-Bleu)/2 points. We also provide a detailed experimental and qualitative analysis of the results.