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
1999-01-01
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 taskFile in questo prodotto:
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