Paper: Online Relative Margin Maximization for Statistical Machine Translation

ACL ID P13-1110
Title Online Relative Margin Maximization for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Recent advances in large-margin learning have shown that better generalization can be achieved by incorporating higher order information into the optimization, such as the spread of the data. However, these so- lutions are impractical in complex struc- tured prediction problems such as statis- tical machine translation. We present an online gradient-based algorithm for rela- tive margin maximization, which bounds the spread of the projected data while max- imizing the margin. We evaluate our op- timizer on Chinese-English and Arabic- English translation tasks, each with small and large feature sets, and show that our learner is able to achieve significant im- provements of 1.2-2 BLEU and 1.7-4.3 TER on average over state-of-the-art opti- mizers with the large feature set.