Machine Learning
Toward a unified approach to statistical language modeling for Chinese
ACM Transactions on Asian Language Information Processing (TALIP)
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Language Model Adaptation Using Mixtures and an Exponentially Decaying Cache
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Exploiting headword dependency and predictive clustering for language modeling
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Minimum sample risk methods for language modeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Language model adaptation with MAP estimation and the perceptron algorithm
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
An empirical study on language model adaptation using a metric of domain similarity
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
An empirical study on language model adaptation
ACM Transactions on Asian Language Information Processing (TALIP)
Approximation lasso methods for language modeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Minimum sample risk methods for language modeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A unified approach to transliteration-based text input with online spelling correction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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This paper presents comparative experimental results on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three discriminative training methods, the boosting algorithm, the average perceptron and the minimum sample risk method, on the task of Japanese Kana-Kanji conversion. We evaluate these techniques beyond simply using the character error rate (CER): the CER results are interpreted using a metric of domain similarity between background and adaptation domains, and are further evaluated by correlating them with a novel metric for measuring the side effects of adapted models. Using these metrics, we show that the discriminative methods are superior to a MAP-based method not only in terms of achieving larger CER reduction, but also of being more robust against the similarity of background and adaptation domains, and achieve larger CER reduction with fewer side effects.