Paper: Factored Markov Translation with Robust Modeling

ACL ID W14-1616
Title Factored Markov Translation with Robust Modeling
Venue International Conference on Computational Natural Language Learning
Year 2014

Phrase-based translation models usually memorize local translation literally and make independent assumption between phrases which makes it neither generalize well on unseen data nor model sentence- level effects between phrases. In this pa- per we present a new method to model correlations between phrases as a Markov model and meanwhile employ a robust smoothing strategy to provide better gen- eralization. This method defines a re- cursive estimation process and backs off in parallel paths to infer richer structures. Our evaluation shows an 1.1?3.2% BLEU improvement over competitive baselines for Chinese-English and Arabic-English translation.