Multilingual Natural Language Processing — Overview
Course: Multilingual Natural Language Processing (5204MNLP6Y) — 2026/27 Sem. 1, Period 1
Programme: MSc Artificial Intelligence, UvA
Credits: 6 EC
Instructor: Christof Monz (Informatics Institute)
Time zone: Europe/Amsterdam
Prerequisites
- Basic math: linear algebra, calculus, probability
- Basic machine learning: train/dev/test split, linear regression, classification
- Basic implementation experience
Course Content
Provides an overview of NLP problems where multilinguality plays a role:
- Multilingual scenarios: NER for multiple languages, language-independent parsing, cross-lingual classification
- Crosslingual scenarios: Machine translation, crosslingual QA, crosslingual unlearning, crosslingual reasoning
This course is NOT: a comprehensive intro to ML or NLP — it focuses specifically on multilingual/crosslingual aspects.
Assessment
| Component | Weight | Deadline | Notes |
|---|
| Mini Project | TBD | Report & code: 2026-10-09 12:00; Presentations: Week 7 | Teams of ~5; pick by 2026-09-04 17:00 |
| Exam | TBD | TBD | |
Notes
- Monz always requires pass on both assignments and the exam. Realistically, you’ll need at least a 5.5 on each.
- The weekly schedule is structure-light; most of the work is project-driven with lectures as supplements.
Weekly Schedule
Week 1 — Overview & NLP Landscape
| Topic | Lecturer | Readings | Notes |
|---|
| L1 | Introduction to NLP, Applications, Course Admin | Monz | — | MNLP-L01 - Overview |
| Key Dates | Team formation & problem selection by Sep 4 | | | |
Week 2 — Project Kick-off
| Topic | Lecturer | Readings | Notes |
|---|
| Project | Literature review, data inspection, baseline model selection | | | |
Week 3 — First Model
| Topic | Lecturer | Readings | Notes |
|---|
| Project | Implement first model, evaluate, debug | | | |
Week 4 — Refinement
| Topic | Lecturer | Readings | Notes |
|---|
| Project | Refine or try alternative model; dropout, layernorm, residual connections | | | |
Week 5 — Error Analysis
| Topic | Lecturer | Readings | Notes |
|---|
| Project | Second model refinement, error analysis, conclusions | | | |
Week 6 — Finalize
| Topic | Lecturer | Readings | Notes |
|---|
| Project | Loose ends, 4-page report, presentation slides | | | |
Week 7 — Presentations
| Topic | Lecturer | Readings | Notes |
|---|
| Project | Team presentations (10-15 min + 5 min Q&A) | | | |
Key Deadlines
| Date | What |
|---|
| 2026-09-04 17:00 | Submit team + problem choice (Google Sheet) |
| 2026-10-09 12:00 | Submit report (4 pp PDF), slides, code (GitHub link) |
| Week 7 | Presentations |
Mini Project — What Makes a Good One
- Good succinct description of most relevant research papers
- Good description of data preprocessing/settings
- Good motivation of neural architecture choices
- Thorough evaluation under different settings/architectures; error analysis
- Report focusing on most relevant findings: what works and what doesn’t, why
- MNLP - Mini Project — full project description, schedule, deliverables, and evaluation criteria
Resources
- Mini Project slides (PDF in Assets/)
- Canvas modules: Overview, Mini Project
- Mini Project slides (PDF in Assets/)
- Canvas modules: Overview, Mini Project