Historical milestone · 2010
Recurrent neural network language model
Reviewed through September 18, 2026
2010 · Historical milestone
Recurrent neural network language model
- Era
- 2010
- Theme
- Language models & representation
- Evidence form
- Evaluated an RNN next-word model and mixtures on speech-recognition corpora against strong backoff n-grams
- School / paradigm
- Neural language modeling
- Institution / context
- Brno University of Technology / Johns Hopkins
- Researchers
- Tomáš Mikolov; Martin Karafiát; Lukáš Burget; Jan Černocký; Sanjeev Khudanpur
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Reported roughly 50% perplexity reduction for RNN mixtures and meaningful word-error-rate improvements, with computation as the main drawback.
Result / historical claim. Training was expensive, models were small by later standards, and recurrent inference was sequential.
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Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Evaluated an RNN next-word model and mixtures on speech-recognition corpora against strong backoff n-grams
Limitation / debate. Modern neural language modeling, learned context, scaling, and the compute-versus-quality frontier.
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