Paper: Reordering Model for Forest-to-String Machine Translation

ACL ID D14-1029
Title Reordering Model for Forest-to-String Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this paper, we present a novel exten- sion of a forest-to-string machine transla- tion system with a reordering model. We predict reordering probabilities for every pair of source words with a model using features observed from the input parse for- est. Our approach naturally deals with the ambiguity present in the input parse forest, but, at the same time, takes into account only the parts of the input forest used by the current translation hypothesis. The method provides improvement from 0.6 up to 1.0 point measured by (Ter ? Bleu)/2 metric.