2022-2023: Deep-Learning and Natural Language Processing, IASD

This course is an introduction to Natural Language Processing (with deep-learning methods). *It is over now ! *


  • The course starts the 10th of Hanuary: 8h30 at PariSantĂ©Campus
  • There will be 3 guest lectures

Registration for the readings and projects

Slides and resources

Expected schedules

It starts in january 2020 (the 16th). The course are scheduled on Tuesday, starting at 8:30 in the morning.

10-jan, course:

NLP, overview and the main tasks

Text classification with NNet

17-jan, course: sequence models

24-jan, course on Advanced models

  • The end of recurrent model, with LSTM
  • Bi-LSTM, Attention
  • Transformer

Some readings:

31-jan, course on Representation learning and contrastive estimation

by Matthieu Labeau

07-feb, course on Syntax !

by Benoit Crabbé

14-feb, Readings

14-march, course on model probing

by Guillaume Wisniewski


The evaluation is in two parts. For both, first make your team (typically 2/3 students).


The goal is to read an article an to make a presentation (the 14-feb). A list will be availble soon, but you can also propose one (I must agree beforehand). Select one article per team to read and analyse the paper to make a clear and synthetic presentation. Some questions you may use to guide your reading are (among others):

  • Did you like the paper? Did you find it interesting? Be honest!
  • What are the most important things you learned from the paper? Why are they important?
  • Do the lessons learned generalize beyond the specific task? Do they contribute towards building an important system or application?
  • Is the experimental setup satisfying? Any experiments missing? Any obvious or important baseline missing?
  • Is the problem/approach well motivated?
  • Are you convinced by the results? Why?
  • Is the writing clear? Is the paper well structured?

The important dates are :

  • Make up your team and select the paper before the 1-February
  • Presentation: the 14-February


A list is available, but you can also propose one (I must agree beforehand).

  • Team and the project registration : before 1-feb
  • Deliverable for the XXX: 2 pages (pdf only) to describe the data, the task and your plan
  • Deliverable for XXX: a github/gitlab repository
  • Final deliverable: a report in pdf and the code via the git repos
  • Final deadline: XXX

    Feel free to use the teams channel to interact with me or with the other groups.

Author: A. Allauzen

Created: 2024-06-24 lun. 17:13