We describe a transformation-based learning method for learning a sequence of monolingual tree transformations that improve the agreement between constituent trees and word alignments in bilingual corpora. Using the manually annotated English Chinese Translation Treebank, we show how our method automatically discovers transformations that accommodate differences in English and Chinese syntax. Furthermore, when transformations 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 improvement over baseline trees.
REF utilized a transformation-based method to learn a sequence of monolingual tree transformations for translation.
Transforming Trees to Improve Syntactic Convergence
EMNLP
Computer Science