Paper: A Comparative Study of Target Dependency Structures for Statistical Machine Translation

ACL ID P12-2020
Title A Comparative Study of Target Dependency Structures for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

This paper presents a comparative study of target dependency structures yielded by sev- eral state-of-the-art linguistic parsers. Our ap- proach is to measure the impact of these non- isomorphic dependency structures to be used for string-to-dependency translation. Besides using traditional dependency parsers, we also use the dependency structures transformed from PCFG trees and predicate-argument structures (PASs) which are generated by an HPSG parser and a CCG parser. The experi- ments on Chinese-to-English translation show that the HPSG parser?s PASs achieved the best dependency and translation accuracies.