Paper: Improving Tree-to-Tree Translation with Packed Forests

ACL ID P09-1063
Title Improving Tree-to-Tree Translation with Packed Forests
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

Current tree-to-tree models suffer from parsing errors as they usually use only 1- best parses for rule extraction and decod- ing. We instead propose a forest-based tree-to-tree model that uses packed forests. The model is based on a probabilis- tic synchronous tree substitution gram- mar (STSG), which can be learned from aligned forest pairs automatically. The de- coder finds ways of decomposing trees in the source forest into elementary trees us- ing the source projection of STSG while building target forest in parallel. Compa- rable to the state-of-the-art phrase-based system Moses, using packed forests in tree-to-tree translation results in a signif- icant absolute improvement of 3.6 BLEU points over using 1-best trees.