This paper presents a method for n-gram language model adaptation based on the principle of minimum discrimination information. A background language model is adapted to fit constraints on its marginal distributions that are derived from new observed data. This work gives a different derivation of the model by Kneser et al. (1997) and extends its application to interpolated language models. The proposed method has been evaluated on an Italian 60K-word broadcast news task

Efficient Language Model Adaptation through MDI Estimation

Federico, Marcello
1999

Abstract

This paper presents a method for n-gram language model adaptation based on the principle of minimum discrimination information. A background language model is adapted to fit constraints on its marginal distributions that are derived from new observed data. This work gives a different derivation of the model by Kneser et al. (1997) and extends its application to interpolated language models. The proposed method has been evaluated on an Italian 60K-word broadcast news task
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/1749
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