Language, Speech and Dialogue Processing

6 EC

Semester 2, period 4

5082LSDP6Y

Owner Bachelor Kunstmatige Intelligentie
Coordinator J.P. Trujillo PhD
Part of Minor Logic and Computation, year 1Bachelor Artificial Intelligence, year 3Bachelor Bèta-gamma, major Kunstmatige Intelligentie, year 3Bachelor Future Planet Studies, major Kunstmatige Intelligentie, year 3

Course manual 2023/2024

Course content

The course intends to provide a comprehensive overview of the fundamentals and most recent approaches to language & dialogue modeling including natural language and speech processing in dialogue systems. An important part of the course is a collaborative learning environment for students to experiment and apply the theoretical knowledge in practice. The students are expected to practice formulating their own research questions, self-organize, plan and distribute the subtasks to effectively work in a team.

Objectives

  • The student has basic knowledge of translation, summarisation, question answering, and dialogue modelling
  • The student can describe the relevant components of NLP systems for translation, summarisation, question answering and dialogue.
  • The student can apply knowledge of statistical modelling to tasks such as machine translation, text summarisation, question answering, dialogue modelling and related problems.
  • The student can analyse the state-of-the-art, identify limitations, and propose ways of overcoming them.
  • The student acknowledges and is critical about the broader impact of NLP technology (e.g., ethical issues, environmental impact, fairness, etc.)

Teaching methods

  • Lecture
  • Working independently on e.g. a project or thesis
  • Supervision/feedback meeting
  • Self-study
  • Presentation/symposium

The students will acquire theoretical knowledge in the lectures, and practice in teams working on a joint project under supervision providing regular feedback and progress monitoring.

Learning activities

Activiteit

Aantal uur

Zelfstudie

168

Attendance

Programme's requirements concerning attendance (OER-B):

  • For practical trainings and tutorials with assignments attendance is obligatory. The requirements for attendance might differ between courses and are stated in the course manual. When students do not meet the requirements for attendance, he or she cannot finish the course with a pass mark.

Assessment

Item and weight Details

Final grade

1 (100%)

Deeltoets

Questions in the final exam can be based on any of the six lectures, as well as the materials covered in the practical sessions (Laptop Colleges).

Assignments

Group Project

  • Projects will be carried out in small groups, with a presentation and written summary due at the end of the course. Both the presentation and the written summary count towards the grade of this assignment.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1 Introduction to Language Models Lecture and tutorials
2 Transformer Models lecture, tutorials, additional reading
3 Transfer Learning lecture, tutorials, additional reading
4 Multimodal DIalogue Modeling lecture, group work
5 Interaction Dynamics and Text-to-Sign  lecture, group work
6 Co-Speech Movement Generation lecture, group work
7 Assessment Exam & Presentation

Contact information

Coordinator

  • J.P. Trujillo PhD