CMU 11-737: Multilingual Natural Language Processing

Course Description

CMU 11-737 is an advanced graduate-level course on natural language processing techniques applicable to many languages. Students who take this course should be able to develop linguistically motivated solutions to core and applied NLP tasks for any language. This includes understanding and mitigating the difficulties posed by lack of data in low-resourced languages or language varieties, and the necessity to model particular properties of the language of interest such as complex morphology or syntax. The course will introduce modeling solutions to these issues such as multilingual or cross-lingual methods, linguistically informed NLP models, and methods for effectively bootstrapping systems with limited data or human intervention. The project work will involve building an end-to-end NLP pipeline in a language you don’t know.

Instructor

Lei Li (Office Hour: Tuesday 4-5pm outside GHC 5417, book a slot here or drop in)

Teaching Assistants

• Simran Khanuja (Office Hour: Friday 4-5pm at GHC 5417)
• Sayali Kandarkar 

Time and Location

Tuesday and Thursday, 2-3:20pm, Doherty Hall 1212 (in-person expected)

Prerequisites

You are highly recommended to take a NLP (11-411 or 11-611 or 11-711) course previously. The assignments for the class will be done by creating neural network models, and examples will be provided using PyTorch. If you are not familiar with PyTorch, we suggest you attempt to familiarize yourself using online tutorials (for example Deep Learning for NLP with PyTorch) before starting the class.

Class Format

For each class there will be:

• Reading: Most classes will have associated reading material that we recommend you read before the class to familiarize yourself with the topic.
• Lecture: The first part of the class with feature a lecture to overview the topic of the day, in which you can ask questions to clarify about any of the material.
• Language in 10: Groups in the class will make a 10-minute presentation of one of the languages of the world.
• Discussion: There will be an open-ended discussion in which we will split into small groups and discuss a question regarding the class.

Homework Submission & Grading

• Participation: Worth 5% of the grade.
• Assignments: There will be 4 assignments (the final one being the project), worth respectively 20%, 20%, 15%, 30% of the grade.
• Language in 10: 10% of grade
• Reading List for Assignment 3 (Blog)
• Project
  

Discussion Forum

We will use the Ed platform for discussions (sign up here), but coming to office hours is also encouraged. You may send private message on edstem platform as well.

Syllabus