Boosting Statistical Word Alignment Using Labeled and Unlabeled Data

This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semisupervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. Based on this semisupervised boosting algorithm, we investigate two boosting methods for word alignment. In addition, we improve the word alignment results by combining the results of the two semi-supervised boosting methods. Experimental results on word alignment indicate that semisupervised boosting achieves relative error reductions of 28.29% and 19.52% as compared with supervised boosting and unsupervised boosting, respectively.