COMP 790.139 (Spr2019): Advanced Topics in NLP: Recent Progress in Different Learning Paradigms

Instructor: Mohit Bansal
Units: 3
Office: FB-246
Lectures: Fridays 3:00pm-5:30pm, Room FB-008
Office Hours: Fridays 2:00pm-3:00pm (by appointment) (FB-246)
Course Webpage:
Course Email: nlpcomp790unc -at-

Syllabus Topics

This course will be an advanced topic seminar class on natural language processing, focusing on the recent advances via diverse learning paradigms such as:

This will be a research-oriented grad-level seminar course, where we will read lots of interesting research papers, brainstorm about ideas on latest research topics, and code and write up fun and novel projects!
Please email me or drop by my office if you have any questions!


Since this is an advanced topics NLP class, the student is expected to have equivalent experience to Dr. Bansal's fall 2016 or fall 2017 regular NLP class.

Grading (tentative)

Grading will consist of:

Details in first class intro lecture slides. There will not be any exams. All submissions should be emailed to:

Lateness Policy

Students are allowed 3 free late days for assignments over the semester. After that, late assignments will be accepted with a 20% reduction in value per day late.

Collaboration Policy

Paper summaries have to be written and submitted individually. Paper presentations have to be done individually. Projects can be individual (e.g., if it relates to your current research) or in pairs (but with proportional work, and with clearly outlined contributions from each team member).

Reference Books

Tentative Schedule

DateTopic Readings Discussion LeadersTodo's
Jan 11 Intro to Class -- Mohit -
Jan 18 Multi-Task Learning 1 (1) Multi-task Sequence to Sequence Learning;
(2) A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks;
(3) Multi-Task Video Captioning with Video and Entailment Generation;
(4) One Model To Learn Them All;
(5) The Natural Language Decathlon: Multitask Learning as Question Answering;
Mohit, Darryl, Yichen -
Jan 25 Multi-Task Learning 2 (1) Deep multi-task learning with low level tasks supervised at lower layers;
(2) Soft, Layer-Specific Multi-Task Summarization with Entailment and Question Generation;
(3) When is multitask learning effective? Semantic sequence prediction under varying data conditions;
(4) Latent Multi-task Architecture Learning;
(5) Dynamic Multi-Level Multi-Task Learning for Sentence Simplification;
Mohit, Xiang, Shiyue, Han -
Feb 1 Reinforcement Learning 1 TBD TBD -
Feb 8 Reinforcement Learning 2 TBD TBD -
Feb 15 Unsupervised Learning, Pretraining, and Fine-Tuning TBD TBD -
Feb 22 Transfer Learning and Domain-Adaptation TBD TBD -
Mar 1 Meta-Learning TBD TBD -
Mar 8 Midterm Project Proposal Presentations -- All -
Mar 15 Spring Break: No Class -- -- -
Mar 22 Adversarial Learning, GANs, Data Augmentation 1 TBD TBD Midterm Write-up Due
Mar 29 Adversarial Learning, GANs, Data Augmentation 2 TBD TBD -
Apr 5 Multi-View Learning TBD TBD -
Apr 12 Active Learning TBD TBD -
Apr 19 University Holiday (No Class) -- -- -
Apr 26 Final Project Presentations (Last Class) -- All -
May 3 Final Write-Up's Due -- All -


The professor reserves the right to make changes to the syllabus, including project due dates. These changes will be announced as early as possible.