Paper: Transforming Trees to Improve Syntactic Convergence

ACL ID D12-1079
Title Transforming Trees to Improve Syntactic Convergence
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

We describe a transformation-based learning method for learning a sequence of mono- lingual tree transformations that improve the agreement between constituent trees and word alignments in bilingual corpora. Using the manually annotated English Chinese Transla- tion Treebank, we show how our method au- tomatically discovers transformations that ac- commodate differences in English and Chi- nese syntax. Furthermore, when transforma- tions are learned on automatically generated trees and alignments from the same domain as the training data for a syntactic MT system, the transformed trees achieve a 0.9 BLEU im- provement over baseline trees.