Paper: Training MRF-Based Phrase Translation Models using Gradient Ascent

ACL ID N13-1048
Title Training MRF-Based Phrase Translation Models using Gradient Ascent
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

This paper presents a general, statistical framework for modeling phrase translation via Markov random fields. The model al- lows for arbituary features extracted from a phrase pair to be incorporated as evidence. The parameters of the model are estimated using a large-scale discriminative training approach that is based on stochastic gradi- ent ascent and an N-best list based expected BLEU as the objective function. The model is easy to be incoporated into a standard phrase-based statistical machine translation system, requiring no code change in the runtime engine. Evaluation is performed on two Europarl translation tasks, German- English and French-English. Results show that incoporating the Markov random field model significantly improves the perfor- mance of a state-of-t...