Proseminar: Computational Pragmatics (Block Course, Spring 2025)

Time & Location: decided at kick-off and via Doodle, indicatively end of March-beginning April
Teacher: Dr Volha Petukhova


Registration in CLOSED

Suitable for: Bachelor CoLi, CS

Pragmatics as a branch of linguistics can be characterized as the study of language use in context and concerns with interpretation of utterance meaning in context. Computational pragmatics is pragmatics with computational means, which include models of dialogue management processes, collections of language use data, annotation schemes and software tools for corpus creation, process models of language generation and interpretation, context representations, and inference methods for context-dependent utterance generation and interpretation processes. Work on computational pragmatics often takes place within research on dialogue systems.

In this proseminar, we will learn how to perform research in computational pragmatics: (1) understand how to compute pragmatic meaning; (2) study the mechanisms underlying the main pragmatic inferences and aspects of pragmatic meaning; (3) discuss algorithms that enable the use of theoretical concepts in practical applications. Focus will be put on computational dialogue modelling for dialogue system design.

Seminar meetings will take in March-April. After paper selection/assignment, each student will give a presentation on a selected topic (30 minutes + 10 minutes questions): dry-runs (ungraded) and final (graded). At the end of the course, each student has to prepare a short report (about 10 pages) and hand it for grading.

Grading: 40% based on the talk, 20% based on discussion participation and assignments; 40% based on the report


Situating Computational Pragmatics

Introduction slides: in Teams

Basic reading:

1. Dialogue Interaction and Conversational Analysis

2. Activity-Based Approach to Pragmatics

3. Dialogue Acts and Context

4. Feedback and Grounding

5.Computational Dialogue Models

6. Standards and Resources for Data-Driven Dialogue Modelling

7. Machine Learning in Dialogue Modelling: Dialogue Act Recognition

8. Machine Learning in Dialogue Modelling: Statistical and Neural Models

9. Authoring tools and development environments

LaTeX template for term papers (zip)
11-point checklist for term papers (pdf)

For any questions, please send an email to:
Use subject tag: [CP_24]