Paper: A Systematic Comparison of Phrase-Based Hierarchical and Syntax-Augmented Statistical MT

ACL ID C08-1144
Title A Systematic Comparison of Phrase-Based Hierarchical and Syntax-Augmented Statistical MT
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

Probabilistic synchronous context-free grammar (PSCFG) translation models define weighted transduction rules that represent translation and reordering oper- ations via nonterminal symbols. In this work, we investigate the source of the im- provements in translation quality reported when using two PSCFG translation mod- els (hierarchical and syntax-augmented), when extending a state-of-the-art phrase- based baseline that serves as the lexical support for both PSCFG models. We isolate the impact on translation quality for several important design decisions in each model. We perform this comparison on three NIST language translation tasks; Chinese-to-English, Arabic-to-English and Urdu-to-English, each representing unique challenges.