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Generalised measures of predictive uncertainty in online language processing

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I will present a family of sampling-based uncertainty measures that generalise surprisal and allow expressing a wider range of hypotheses about the workings of incremental language processing. I will show how these uncertainty measures, estimated via Monte Carlo sampling with neural network language models, exhibit predictive power comparable or superior to surprisal for a range of human neural and behavioural responses to incremental linguistic input.

Speaker Bio: Mario Giulianelli is an Associate Professor of Computational Linguistics at UCL , Director of the UCL Computational Linguistics Master’s programme, and a Member of the European Laboratory for Learning and Intelligent Systems. His research explores the information processing principles underlying the ability to understand, produce, learn, and use language in interaction—both in human and in artificial language processing systems.

This talk is part of the NLIP Seminar Series series.

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