Paper: Jointly Optimizing a Two-Step Conditional Random Field Model for Machine Transliteration and Its Fast Decoding Algorithm

ACL ID P10-2051
Title Jointly Optimizing a Two-Step Conditional Random Field Model for Machine Transliteration and Its Fast Decoding Algorithm
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

This paper presents a joint optimization method of a two-step conditional random field (CRF) model for machine transliter- ation and a fast decoding algorithm for the proposed method. Our method lies in the category of direct orthographical map- ping (DOM) between two languages with- out using any intermediate phonemic map- ping. In the two-step CRF model, the first CRF segments an input word into chunks and the second one converts each chunk into one unit in the target language. In this paper, we propose a method to jointly op- timize the two-step CRFs and also a fast algorithm to realize it. Our experiments show that the proposed method outper- forms the well-known joint source channel model (JSCM) and our proposed fast al- gorithm decreases the decoding time sig- nificantly. Furthermore...