Topic adaptation for language modeling is concerned with adjusting the probabilities in a language model to better reflect the expected frequencies of topical words for a new document. We present a novel technique for adapting a language model to the topic of a document, using a nonlinear interpolation of n-gram language models. A three-way, mutually exclusive division of the vocabulary into general, on-topic and off-topic word classes is used to combine word predictions from a topic-specific and a general language model. We achieve a slight decrease in perplexity and speech recognition word error rate on a Broadcast News test set using these techniques. Our results are compared to results obtained through linear interpolation of topic models.
Cite as: Seymore, K., Chen, S., Rosenfeld, R. (1998) Nonlinear interpolation of topic models for language model adaptation. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0897, doi: 10.21437/ICSLP.1998-667
@inproceedings{seymore98_icslp,
author={Kristie Seymore and Stanley Chen and Ronald Rosenfeld},
title={{Nonlinear interpolation of topic models for language model adaptation}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0897},
doi={10.21437/ICSLP.1998-667}
}