Multilingual Representation Learning

  • Course code: COMP 598 002(Winter 2025)
  • Instructors:
  • Classroom: McConnell Engineering Building 103
  • Time: Tuesdays & Thursday, 10:05am – 11:25am
  • Links: Course web page

Description

This course is a seminar-style course that focuses on advances in multilingual representation learning and how to scale language technologies to many languages of the world including high-resource languages (e.g. English and French), mid-resource languages (e.g. Indonesia and Swahili) and low-resource languages (e.g. Wolof and Quechua) , and some multimodal applications to images and speech. In the first four lectures, I will provide an overview of multilingual NLP, text embedding models, cross-lingual transfer learning and open problems in NLP.

Prerequisites

One of the following McGill courses: Natural Language Processing (COMP 550), Natural Language Understanding with Deep Learning (COMP 545), Applied Machine Learning (COMP 551), or a relevant NLP course at other universities. If you are unsure, email me.

Grading (tentative)

This is a demanding course in terms of participation and projects. All deadlines start after the Add/Drop deadline (Tuesday January 14, 2025)

● Reading and Reviewing papers: (20%): You are expected to submit technical reviews (conference style reviews) for one of the paper prior to each class on MyCourses. You will submit 8 such reviews.

● Presenting papers in class (20%): After the first few lectures, each student should form a group of two for joint presentation. Not on the same topic you wrote a technical review for. 

● Leading paper discussions in a class (10%): Sign up as panelists for at least twice to critically analyze the presented papers.

● In-class Project Proposal presentation (5%): To get quick feedback on your proposed project. 

● Project (40%):  You will do a project in groups of two. This involves

    Literature review (10%)

    Baselines (5%)

    Final paper with new experiments and code submission (15%)

    Final presentation at the end of term (10%)

● Class participation (5%): how engaged in the lectures you are (asking questions during the lectures, coming to office hours etc).