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IJCNLP
2005
Springer

An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity

8 years 10 months ago
An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity
Abstract. This paper presents an empirical study on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three discriminative training models, in the application of Japanese Kana-Kanji conversion. We compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains. In particular, we attempt to interpret the results given in terms of the character error rate (CER) by correlating them with the characteristics of the adaptation domain measured using the information-theoretic notion of cross entropy. We show that such a metric correlates well with the CER performance of the adaptation methods, and also show that the discriminative methods are not only superior to a MAPbased method in terms of achieving larger CER reduction, but are also more robust against the similarity of background and adaptation domains.
Wei Yuan, Jianfeng Gao, Hisami Suzuki
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where IJCNLP
Authors Wei Yuan, Jianfeng Gao, Hisami Suzuki
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