Title: Language Use in Humans and Machines

Abstract: Our ability to understand and produce language is one of humans’ most impressive skills. We use language to exchange information, talk about the world, coordinate our actions, and establish rapport, among a myriad of other things. Computational Linguistics is a branch of Artificial Intelligence that makes use of machine learning methods to study human language and build computational models for automatic natural language processing. How can theories from linguistics and cognitive science inform AI models? And what can we learn about our own linguistic skills from such artificial systems? In this lecture, I will present some of our recent research using foundation models: self-supervised models trained on large quantities of data at scale. In the first part, I will focus on large language models (foundation models trained only on text) and show how they can be used to predict and explain human reading times and utterance acceptability judgements. In the second part, I will consider language-and-vision foundation models trained on text plus images; I will evaluate the representations learned by these models against human linguistic intuitions and sketch work in progress that aims to exploit such models to shed light on how the brain represents semantic knowledge across the linguistic and visual modalities.

Profile: Raquel Fernández is Professor of Computational Linguistics and Dialogue Systems at the Institute for Logic, Language and Computation, University of Amsterdam. She received her PhD from King's College London and before moving to Amsterdam held research positions at Stanford University and the University of Potsdam. She is currently the recipient of a European Research Council (ERC) Consolidator Grant. Raquel’s interests revolve around language use in interaction, conversational AI, language variability and change, and visually grounded language. Her research lab carries out work on these topics at the interface of cognitive science and artificial intelligence. Personal Website

Language: English, no interpretation.

Target audience: General audience / everyone at OIST and beyond.
Freely accessible to all OIST members and guests without registration.

This talk will also be broadcast online via Zoom:

Join Zoom
Meeting ID:     964 7256 3222
Passcode:    393899

※ Please note that this event may be recorded and the videos uploaded. In addition, photos may be taken during the event. These are intended for publication online (the OIST website, social media, etc.)※