Paper: Machine Translation System Combination by Confusion Forest

ACL ID P11-1125
Title Machine Translation System Combination by Confusion Forest
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

The state-of-the-art system combination method for machine translation (MT) is based on confusion networks constructed by aligning hypotheses with regard to word similarities. We introduce a novel system combination framework in which hypotheses are encoded as a confusion forest, a packed forest representing alternative trees. The forest is generated using syntactic consensus among parsed hypotheses: First, MT outputs are parsed. Second, a context free grammar is learned by extracting a set of rules that con- stitute the parse trees. Third, a packed forest is generated starting from the root symbol of the extracted grammar through non-terminal rewriting. The new hypothesis is produced by searching the best derivation in the forest. Experimental results on the WMT10 system combination share...