A computational model of language production
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In this talk, I present a framework that models language production as a mapping from explicit, structured meaning representations to surface sentences. The framework combines an explicit symbolic grammar with unsupervised learning algorithms, allowing us to ask how far structured meaning by itself can drive acquisition.
If we view the framework as a computational proxy for language production and acquisition, we can encode different grammatical theories as alternative grammar modules, expose them to the same data, and compare how they learn and process. I will briefly illustrate this with a case study contrasting a lexicalist grammar with a construction-based one, showing how their characteristic behaviours emerge within the same simulation.
Yuan Gao is a PhD Candidate at the University of Cambridge.
This talk is part of the NLIP Seminar Series series.
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