Truth conditions at scale, and beyond

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• Guy Edward Toh Emerson (University of Cambridge)
• Friday 18 October 2024, 12:00-13:00
• Zoom link: https://cam-ac-uk.zoom.us/j/4751389294?pwd=Z2ZOSDk0eG1wZldVWG1GVVhrTzFIZz09.

If you have a question about this talk, please contact Suchir Salhan.

Truth-conditional semantics has been successful in explaining how the meaning of a sentence can be decomposed into the meanings of its parts, and how this allows people to understand new sentences. In this talk, I will show how a truth-conditional model can be learnt in practice on large-scale datasets of various kinds (textual, visual, ontological), and how this provides empirical benefits compared to non-truth-conditional models. I will then take stock of the bigger picture, and argue it is (unfortunately) computationally intractable to reduce all kinds of language understanding to truth conditions. To enable a more complete account, I will sketch a new approach to approximate Bayesian modelling, with the potential to explain how patterns of language use arise as a result of resource-bounded minds interacting with a computationally demanding world.

Speaker Biography

Guy Emerson is a computational semanticist at the University of Cambridge. He pursues research as an academic fellow at the Department of Computer Science & Technology, pursues teaching as a college lecturer at Gonville & Caius College, and promotes interdisciplinary work as an executive director of Cambridge Language Sciences. He also supports revitalisation of the Hokkien language, and enjoys ballroom and latin dancing.

This talk is part of the NLIP Seminar Series series.

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