Reranking with multiple features for better transliteration

Authors:
Yan Song;Chunyu Kit;Hai Zhao
Affiliations:
City University of Hong Kong, Kowloon, Hong Kong;City University of Hong Kong, Kowloon, Hong Kong;City University of Hong Kong, Kowloon, Hong Kong and Shanghai Jiao Tong University, Shanghai, China
Venue:
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Year:
2010

Citing 5
Cited 2

Minimum error rate training in statistical machine translation

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms

EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A joint source-channel model for machine transliteration

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Machine transliteration using target-language grapheme and phoneme: multi-engine transliteration approach

NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Transliteration of name entity via improved statistical translation on character sequences

NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration

Report of NEWS 2010 transliteration generation shared task

NEWS '10 Proceedings of the 2010 Named Entities Workshop
English-Korean named entity transliteration using substring alignment and re-ranking methods

NEWS '12 Proceedings of the 4th Named Entity Workshop

Quantified Score

Hi-index	0.00

Visualization

Abstract

Effective transliteration of proper names via grapheme conversion needs to find transliteration patterns in training data, and then generate optimized candidates for testing samples accordingly. However, the top-1 accuracy for the generated candidates cannot be good if the right one is not ranked at the top. To tackle this issue, we propose to rerank the output candidates for a better order using the averaged perceptron with multiple features. This paper describes our recent work in this direction for our participation in NEWS2010 transliteration evaluation. The official results confirm its effectiveness in English-Chinese bidirectional transliteration.