Technical - Partner Event
AI Seminar – Alex Murphy
When
Aug. 19, 2022 - Aug. 19, 2022
12:00 PM – 1:00 PM MST
Where
Online
Online
Presenters: Alex Murphy, University of Birmingham
Title: Brain + NLP > NLP?: Towards the incorporation of human brain data into Natural Language Processing
Abstract: 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). In this talk I will recount my journey tackling these issues during my PhD, using models to decode linguistic and semi-linguistic information from single-trial EEG data, touching on various training methods that I have shown boost performance over directly training on single-trial EEG data. This work forms the core of my recent paper at ACL 2022 entitled, “Decoding PoS from Human EEG”. A key theme present throughout this talk is the effect of confounding that arises when working with linguistic data, both in terms of the linguistic status of stimuli (that also generate strong neural responses), but also biological confounds such as eye-movements, whose interference can be even further pronounced in EEG data. I will then summarize some of the main issues I believe still face us and reflect on how these might be surmounted by utilizing recent developments in multimodal Transformer networks and prediction-based representation learning in Reinforcement Learning. These techniques afford us a bridge in which both cognitive neuroscience and machine learning can jointly benefit from advances in the intersection of both domains.
Bio: Alex has recently completed his PhD at the University of Birmingham (UK) in Cognitive Neuroscience & NLP. His interests span many varied topics across (neuro)linguistics, cognitive neuroscience, machine learning and natural language processing. He holds a Bachelor's degree in Linguistics (Bangor University, Wales), alongside Masters degrees in Language Technology (University of Iceland) and IT & Cognition (University of Copenhagen, Denmark). During his PhD he was a Technical Intern on the Language Team at Google Brain (London) where he worked on analyzing brain data with state-of-the-art deep learning models. He is interested in how (human) neural data can be incorporated into natural language processing applications and more widely in other domains of artificial intelligence.
The University of Alberta Artificial Intelligence (AI) Seminar is a weekly meeting where researchers (including students, developers, and professors) interested in AI can share their current research. Presenters include local speakers from the University of Alberta and industry as well as other institutions. The seminars discuss a wide range of topics related in any way to Artificial Intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems of interest. Learn more at the AI Seminar website and by subscribing to the mailing list!
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