Steven Bedrick

Noisy Neural Language Modeling for Typing Prediction in BCI Communication

Rui Dong, David Smith, Shiran Dudy, Steven Bedrick
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies, Jun 2019


Language models have broad adoption in predictive typing tasks. When the typing history contains numerous errors, as in open-vocabulary predictive typing with brain-computer interface (BCI) systems, we observe significant performance degradation in both n-gram and recurrent neural network language models trained on clean text. In evaluations of ranking character predictions, training recurrent LMs on noisy text makes them much more robust to noisy histories, even when the error model is misspecified. We also propose an effective strategy for combining evidence from multiple ambiguous histories of BCI electroencephalogram measurements.

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