Paper: Tuning as Ranking

ACL ID D11-1125
Title Tuning as Ranking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

We offer a simple, effective, and scalable method for statistical machine translation pa- rameter tuning based on the pairwise approach to ranking (Herbrich et al., 1999). Unlike the popular MERT algorithm (Och, 2003), our pairwise ranking optimization (PRO) method is not limited to a handful of parameters and can easily handle systems with thousands of features. Moreover, unlike recent approaches built upon the MIRA algorithm of Crammer and Singer (2003) (Watanabe et al., 2007; Chi- ang et al., 2008b), PRO is easy to imple- ment. It uses off-the-shelf linear binary classi- fier software and can be built on top of an ex- isting MERT framework in a matter of hours. We establish PRO’s scalability and effective- ness by comparing it to MERT and MIRA and demonstrate parity on both phrase-bas...