AI Seminar Series 2022: Alex Murphy

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On August 19, Alex Murphy — a Ph.D. student from the University of Birmingham — presented "Brain + NLP > NLP? Towards the incorporation of human brain data into Natural Language Processing" at the AI Seminar.

Modern machine learning techniques have been shown to successfully encode / decode linguistic information from brain signals. It therefore seems a natural next step to use neurolinguistic data in ML models as an additional input stream to many NLP tasks. In the domain of vision, it has been shown that by forcing models to predict neural data (as well as the learned similarity representations from brain signals), models can become more robust and make “better / more natural mistakes”.

This begs the question whether this effect transfers over to the domain of language / NLP with human data. This additional input stream provides many desirable properties, as the modelling process is less susceptible to idiosyncrasies of a single input modality (e.g. covariate shift, adversarial examples and non-robustness). Murphy recounts his journey tackling these issues during his PhD, using models to decode linguistic and semi-linguistic information from single-trial EEG data, touching on various training methods that he has shown boost performance over directly training on single-trial EEG data.

Watch the full presentation below:

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