Paper: Discriminative Methods For Transliteration

ACL ID W06-1672
Title Discriminative Methods For Transliteration
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006

We present two discriminative methods for name transliteration. The methods correspond to local and global modeling approaches in modeling structured output spaces. Both methods do not require alignment of names in different lan- guages – their features are computed di- rectly from the names themselves. We perform an experimental evaluation of the methods for name transliteration from three languages (Arabic, Korean, and Russian) into English, and compare the methods experimentally to a state-of-the- art joint probabilistic modeling approach. We find that the discriminative methods outperform probabilistic modeling, with the global discriminative modeling ap- proach achieving the best performance in all languages.