Multi-View Co-Training of Transliteration Model

This paper discusses a new approach to training of transliteration model from unlabeled data for transliteration extraction. We start with an inquiry into the formulation of transliteration model by considering different transliteration strategies as a multi-view problem, where each view exploits a natural division of transliteration features, such as phonemebased, grapheme-based or hybrid features. Then we introduce a multi-view Cotraining algorithm, which leverages compatible and partially uncorrelated information across different views to effectively boost the model from unlabeled data. Applying this algorithm to transliteration extraction, the results show that it not only circumvents the need of data labeling, but also achieves performance close to that of supervised learning, where manual labeling is required for all training samples.