Paper: Online Large-Margin Training of Syntactic and Structural Translation Features

ACL ID D08-1024
Title Online Large-Margin Training of Syntactic and Structural Translation Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

Minimum-error-rate training (MERT) is a bot- tleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably opti- mize. Building on the work of Watanabe et al., we explore the use of the MIRA algorithm of Crammer et al. as an alternative to MERT. We first show that by parallel processing and exploiting more of the parse forest, we can obtain results using MIRA that match or sur- pass MERT in terms of both translation qual- ity and computational cost. We then test the method on two classes of features that address deficiencies in the Hiero hierarchical phrase- based model: first, we simultaneously train a large number of Marton and Resnik’s soft syn- tactic constraints, and, second, we introduce a novel structural distorti...