Unsupervised Constraint Driven Learning For Transliteration Discovery

This paper introduces a novel unsupervised constraint-driven learning algorithm for identifying named-entity (NE) transliterations in bilingual corpora. The proposed method does not require any annotated data or aligned corpora. Instead, it is bootstrapped using a simple resource – a romanization table. We show that this resource, when used in conjunction with constraints, can efficiently identify transliteration pairs. We evaluate the proposed method on transliterating English NEs to three different languages Chinese, Russian and Hebrew. Our experiments show that constraint driven learning can significantly outperform existing unsupervised models and achieve competitive results to existing supervised models.