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Classes for fast maximum entropy training
Open accessAbstract
Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a speedup technique: we change the form of the model to use classes. Our speedup works by creating two maximum entropy models, the first of which predicts the class of each word, and the second of which predicts the word itself. This factoring of the model leads to fewer nonzero indicator functions, and faster normalization, achieving speedups of up to a factor of 35 over one of the best previous techniques. It also results in typically slightly lower perplexities. The same trick can be used to speed training of other machine learning techniques, e.g. neural networks, applied to any problem with a large number of outputs, such as language modeling.
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(1992) · doi:10.5555/176313.176316 · Cited by 2,892
Cited by
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Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., & Robinson, T. (2013) · arXiv · doi:10.48550/arxiv.1312.3005 · Cited by 1,051
Wang, T., & Isola, P. (2020) · arXiv · doi:10.48550/arxiv.2005.10242 · Cited by 921
(2005) · Cited by 823
Metadata
- Authors
- Goodman, J.
- Venue
- 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
- Publisher
- IEEE
- Published
- 2002-11-13
- Volume
- 1
- Pages
- 561-564
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2100714283
- Cited by
- 219
- References
- 1
- FWCI
- 2.99
- Type
- conference paper
- Language
- en
- Open access
- green
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- Goodman, J. (2002). Classes for fast maximum entropy training. 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221). https://doi.org/10.1109/icassp.2001.940893
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