Paper: Improved Reordering for Phrase-Based Translation using Sparse Features

ACL ID N13-1003
Title Improved Reordering for Phrase-Based Translation using Sparse Features
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

There have been many recent investigations into methods to tune SMT systems using large numbers of sparse features. However, there have not been nearly so many examples of helpful sparse features, especially for phrase- based systems. We use sparse features to ad- dress reordering, which is often considered a weak point of phrase-based translation. Using a hierarchical reordering model as our base- line, we show that simple features coupling phrase orientation to frequent words or word- clusters can improve translation quality, with boosts of up to 1.2 BLEU points in Chinese- English and 1.8 in Arabic-English. We com- pare this solution to a more traditional max- imum entropy approach, where a probability model with similar features is trained on word- aligned bitext. We show that sparse d...