Abstract

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.