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Improved backing-off for M-gram language modeling
Abstract
In stochastic language modeling, backing-off is a widely used method to cope with the sparse data problem. In case of unseen events this method backs off to a less specific distribution. In this paper we propose to use distributions which are especially optimized for the task of backing-off. Two different theoretical derivations lead to distributions which are quite different from the probability distributions that are usually used for backing-off. Experiments show an improvement of about 10% in terms of perplexity and 5% in terms of word error rate.
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LLaMA: Open and Efficient Foundation Language Models
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Sundermeyer, M., Schlüter, R., & Ney, H. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-65 · Cited by 1,963
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Metadata
- Authors
- Kneser, R., & Ney, H.
- Venue
- 1995 International Conference on Acoustics, Speech, and Signal Processing
- Publisher
- IEEE
- Published
- 2002-11-19
- Volume
- 1
- Pages
- 181-184
- OpenAlex
- W1934041838
- Cited by
- 1,473
- References
- 0
- FWCI
- 16.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kneser, R., & Ney, H. (2002). Improved backing-off for M-gram language modeling. 1995 International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/icassp.1995.479394
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