Abstract
This paper presents a transliteration system based on pair Hidden Markov Model (pair HMM) training and Weighted Finite State Transducer (WFST) techniques. Parameters used by WFSTs for transliteration generation are learned from a pair HMM. Parameters from pair-HMM training on English-Russian data sets are found to give better transliteration quality than parameters trained for WFSTs for corresponding structures. Training a pair HMM on English vowel bigrams and standard bigrams for Cyrillic Romanization, and using a few transformation rules on generated Russian transliterations to test for context improves the system's transliteration quality.
Cite
CITATION STYLE
Nabende, P. (2009). Transliteration system using pair HMM with weighted FSTs. In NEWS 2009 - 2009 Named Entities Workshop: Shared Task on Transliteration at the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009 (pp. 100–103). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1699705.1699731
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
