Dr Goldwater is a Reader in the Institute for Language, Cognition and Computation at the University of Edinburgh’s School of Informatics. She received her PhD in 2007 from Brown University, supervised by Mark Johnson, and spent two years as a postdoctoral researcher at Stanford University before moving to Edinburgh. Her research interests include unsupervised learning for natural language processing, computer modelling of language acquisition in children, and computational studies of language use. Dr. Goldwater holds a Scholar Award from the James S McDonnell Foundation for her work on “Understanding synergies in language acquisition through computational modelling” and is the 2016 recipient of the Roger Needham Award from the British Computer Society for “distinguished research contribution in computer science by a UK-based researcher who has completed up to 10 years of post-doctoral research.”
Talk title: Language learning in humans and machines: making connections to make progress
Synopsis: Current language processing methods are resource-intensive and available for only a tiny fraction of the world’s 5000 or more languages, mainly those spoken in large rich countries. This talk will argue that in order to solve this problem, we need a
better understanding of how humans learn and represent language in our minds, and we need to consider how human-like learning biases can be built into computational systems. Sharon Goldwater will illustrate these ideas using examples from her own research. She will discuss why language is such a difficult problem, what we know about human language learning, and then show how her own work has taken inspiration from that to develop better methods for computational language learning.
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