Modern natural language processing

Summary

Natural language processing is ubiquitous in modern intelligent technologies, serving as a foundation for language translators, virtual assistants, search engines, and many more. In this course, students will learn algorithmic tools for tackling problems in modern NLP.

Content

Learning Prerequisites

Recommended courses

• CS-233a or CS-233b Introduction to machine learning
• CS-433 Machine learning

Important concepts to start the course

- Python programming
- Probability and Statistics
- Linear Algebra
- Machine Learning concepts

Learning Outcomes

By the end of the course, the student must be able to:

• Define basic problems and tasks in natural language processing (e.g., machine translation, summarization, text classification, language generation, sequence labeling, informaiton extraction, question answering)
• Implement common modern approaches for tackling NLP problems and tasks (embeddings, recurrent neural models, attentive neural models) and how to train them
• Understand failure modes of these models and learning algorithms (e.g., robustness, interpretability/explainability, bias, evaluation)
• Review academic research papers and understand their contributions, strengths, and weaknesses according to the principles learned in lecture
• Complete a project that applies these algorithms to a real-world NLP problem, where they will define a task, evaluation, model implementation, and analyze the shortcomings of their approach

Teaching methods

• Lectures
• Lab sessions
• Paper reading
• Course project

Expected student activities

• Attend lectures and participate in class
• Complete homework assignments
• Complete a review of a research paper of their choosing published at an NLP conference over the last 5 years
• Complete a project of their choosing (agreed upon with course supervisor) : complete a project proposal outlining topic and evaluation plan; submit two project milestones; submit final project report; present project findings to committee of course instructor and TAs.

Assessment methods

• Assignments (40%)
• Group Project (60%)

Supervision

Resources

Moodle Link

• https://go.epfl.ch/CS-552