Computational language modeling and cognition (Master S3 Class)

Basic information

• Credits: 3
• Format: 2 hours weekly
• 2026-2027 instructors: Benoît Crabbé
• 2026-2027 schedule: Thursdays, 16:15-18:15, room 309
• Moodle page

Description

This course introduces to research oriented questions at the intersection of computational linguistics, linguistics and cognitive science. It will in particular focus on topics in mathematical and computational modeling of language and their interaction with experimental methods.
The class will focus on topics such as human sentence processing, language evolution, language emergence or language acquisition among others. The course storyline will put some focus on models that support the hypothesis that language is an efficient communication system. For each of these topics, we examine and describe how traditional and current computational models contribute to feed scientific theory.

Prerequisites

Basics in Machine Learning, Deep Learning and language modeling
Good programming skills and fluency with numerical libraries (such as numpy or pytorch)
Notions of psycholinguistics will help but are not mandatory

Learning outcomes

On successful completion of this course, students should be able to:

• Better understand the role of computational models for supporting research in experimental and computational linguistics
• Acquire notions of explainable AI and AI for science
• Read, analyze, criticize and report on scientific literature
• Leverage deep learning models for research in computational linguistics