Advanced Topics in Natural Language Processing
Lectures and Schedules
In this course we will teach advanced topics in natural language processing, ranging from general techniques such as deep learning for NLP to specific topics such as information extraction, question answering, reading comprehension, summarization, dialogue systems, and natural language generation. Review of classic as well as state-of-the-art techniques and remaining challenges, and exploration of recent proposals for meeting these challenges. Intended for graduate students doing research in natural language processing.
- The instructor gives an overview tutorial (50-60mins), followed by Q/A and paper discussions (15-25mins).
- 4 Assignments (40 pts in total): participate in shared tasks and submit through Codalab, the grades will be based
your system’s rank in the class. If a submission is successfully complete then it will get 5 basic points,
and then the additional points are based on the system's relative rank in the class between [0, 2], and 0-3 points will be assigned to analysis and report writing. Well-written error analysis and discussions will receive extra points.
- Term Project (55 pts in total, 5pts proposal, 25pts project implementation, 25pts final report writing/presentation) Team work is encouraged, but a team cannot be more than 4 students. Everyone is encouraged to submit papers to NAACL/ACL based on the term projects.
- Reading assignment (5 points each session): Each student is required to sign up one paper critique session, 3mins with or without slides.
The instructor will write most of the slides and hand-outs, give survey about the best papers from
The following books may provide some useful background:
- Programming in Python
- Already took NLP and machine learning courses
- Familiar with Probability and Statistics
- Research background in NLP or related fields; it's targeted for students who work on related areas.
- Interests in languages and lingusitics
- Solid background in algorithms
- Good programming skills
- Sufficient mathematical background
- Restricted Academic Integrity
- NO Incompleteness are accepted
- NO Late Assignments are accepted
- NO cell phones and internet surfing are allowed in the lecture room
- Don't come late to the class, if you are late more than 10 minutes, simply skip it in order not to disturb
Office Hours and Paper Critique
- Please sign up for office hours and find room information here.
- Please sign up for paper critiques here.