Abstract

Language allows us to package our thoughts into symbolic forms and transmit some approximation of them into each other’s minds. We do this hundreds of times a day as listeners, speakers, readers, and writers. How we’re able to achieve this is one of the great scientific questions in the study of mind and brain. In this talk I describe two of our research group’s recent theoretical and empirical advances in our work on this question. First, we evaluate and calibrate contemporary deep-learning models for human-like processing using numerous controlled experimental benchmarks and human behavioral datasets. Our results shed light on classic questions of the learnability of syntactic structures from linguistic input, and also highlight the continued importance of model architecture for human-like linguistic generalization. Second, we offer new results from a theory of how memory constrains human understanding: namely, that context representations are “lossy” in an information-theoretic sense. This theory novelly links memory representations for grammatical structures to the statistics of the natural language environment, explaining recent results showing how the same grammatical configuration can differ in difficulty for native speakers of different languages. The theory also predicts new generalizations about word order that we empirically confirm in multiple languages.

Joining instructions: Georgi Karadzhov is inviting you to a scheduled Zoom meeting.

Topic: NLP Seminar 12.11.2021 Time: Nov 12, 2021 12:00 PM London

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