EECS 598: Special Topics on Natural Language Processing with Deep Learning

Time and Location: Mondays and Wednesdays 12pm - 1:30pm, online via Zoom (Zoom link is provided on Piazza, anyone with umich email can sign up and is welcomed to join the discussions)

Instructor: Lu Wang

Staff and Office Hours:

Discussion Forum: Piazza, sign up at

Course Description (and Syllabus)

Deep learning models have made impressive progress in natural language understanding and generation problems, such as automatic text summarization and dialogue systems. In this research-oriented seminar course, we will focus on the discussion of recent advances of using deep learning models for solving natural language generation (NLG) problems. Topics include text summarization, long-form text generation, dialogue systems, question generation, NLG evaluation, and bias and ethics issues in generation systems. The class takes the following format: Students will present and discuss papers, and work on course projects in small groups. After taking this course, students are expected to gain knowledge in major tasks and the state-of-the-arts in NLG, how to design and evaluate NLG systems, how to evaluate research papers in the area, and how to implement and evaluate their own idea.

Note: Students who have not taken any NLP course before and would like to learn fundamental concepts and techniques in NLP should consider EECS 595 (graduate-level NLP, offered every fall or winter semester) or EECS 495 (undergraduate-level NLP, scheduled in winter 2021).

Please find the syllabus here: [Link]


This course does not have any required textbook, but you can refer to the following resources.


This course is designed for graduate students and senior undergraduate students majoring computer science, computational linguistics, and other relate areas. Concretely,


Each assignment or report is due by the end of day on the corresponding due date (i.e. 11:59pm, EST). Canvas is used for electronic submission. Assignment or report turned in late will be charged 20 points (out of 100 points) off for each late day (i.e. every 24 hours). Each student has a budget of 5 days throughout the semester before a late penalty is applied. You may want to use it wisely, e.g. save for emergencies. Each 24 hours or part thereof that a submission is late uses up one full late day. Late days are not applicable to final presentation. Each group member is charged with the same number of late days, if any, for their submission. There is no need to inform the instructors if late days are used; timestamp of the last submission on Canvas will be used for automatic grade calculation.

Grades will be determined based on paper critiques and presentation, project, and participation:

Sample Projects

Here are some sample ideas that are related to the topics discussed in this course, you should feel free to explore other research directions based on your own interests.

Schedule (tentative)

Aug 31 (introduction)

Theme: Text Summarization

Sep 2 (neural abstractive summarization)

Sep 7 (no class, labor day)

Sep 9 (reinforcement learning for summarization)

Sep 14 (project discussion with instructor)

Sep 16 (no class and office hour)

Sep 21 (unsupervised abstractive summarization)

Sep 23 (errors in summaries and evaluation)

Theme: Text Generation

Sep 28 (large language models)

Sep 30 (knowledge-enhanced generation)

Oct 5 (content planning for long-text generation)

Oct 7 (non-monotonic decoding)

Oct 12 (issues in neural generation)

Topic: Project Mid-term Presentation

Oct 14

Oct 19

Theme: Dialogue Generation

Oct 21 (goal-oriented dialogues with knowledge augmentation)

Oct 26 (personalized dialogue agents)

Oct 28 (chitchat)

Theme: Question Generation

Nov 2 (factoid question generation)

Nov 4 (open-ended/complex question generation)

Theme: NLG Evaluation

Nov 9 (issues in automatic metrics)

Nov 11 (reference-based evaluation)

Nov 16 (reference-free evaluation)

Theme: Bias and Ethics for Neural Generation

Nov 18 (can human tell machine-generated text?)

Nov 30 (ethics in neural generation)

Topic: Project Final Presentation

Dec 2

Dec 7