Paper: Learning Hierarchical Translation Spans

ACL ID D14-1022
Title Learning Hierarchical Translation Spans
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

We propose a simple and effective ap- proach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft con- straints. Compared to syntactic con- straints, our model is directly acquired from an aligned parallel corpus and does not require parsers. Rich source side contextual features and advanced machine learning methods were utilized for this learning task. The proposed approach was evaluated on NTCIR-9 Chinese-English and Japanese-English translation tasks and showed significant improvement over the baseline system.