Paper: Improving Word Alignment by Semi-Supervised Ensemble

ACL ID W10-2917
Title Improving Word Alignment by Semi-Supervised Ensemble
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010

Supervised learning has been recently used to improve the performance of word alignment. However, due to the limited amount of labeled data, the performance of ”pure” supervised learning, which only used labeled data, is limited. As a re- sult, many existing methods employ fea- tures learnt from a large amount of unla- beled data to assist the task. In this pa- per, we propose a semi-supervised ensem- ble method to better incorporate both la- beled and unlabeled data during learning. Firstly, we employ an ensemble learning framework, which effectively uses align- ment results from different unsupervised alignment models. We then propose to use a semi-supervised learning method, namely Tri-training, to train classifiers us- ing both labeled and unlabeled data col- laborativelyandfurther...