LIN 393

Discourse Processing and Natural Language Generation

The University of Texas at Austin
Fall 2020
Instructor: Jessy Li



Course Information

Course Description

To generate coherent and meaningful text involves careful organization of its content; similarly, to fully understand a text as a whole requires information that cannot be obtained when considering each sentence individually. This seminar explores the intersection of discourse structure and natural language generation. We will discuss foundations of discourse: how one span of text relates to each other leading to the local coherence of the text. We will seek to understand the role of discourse structure in natural language generation, and in end tasks such as summarization and data-to-text generation.


Graduate standing. Prior coursework in Computational Linguistics/Natural Language Processing/Machine Learning/a related field in AI, or instructor consent.


There will be no textbook for this seminar; reading material will consists of technical papers discussed in each meeting.

Organization and Content

Grading Policy

Extension Policy

All extensions will be negotiated on a case-by-case basis. You must talk to the instructor before the deadline in question. If you did not obtain a permission on extension, then by default, 10 points (out of 100) will be deducted for lateness, plus an additional 5 point for every 24-hour period beyond 2 that the assignment is late.

Academic Dishonesty Policy

You are encouraged to discuss assignments with classmates. But all written work must be your own. Students caught cheating will automatically fail the course. If in doubt, ask the instructor.

Notice about students with disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities.

Notice about missed work due to religious holy days

A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.

FERPA and Class Recordings

Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.

COVID Guidance

To help keep everyone at UT and in our community safe, it is critical that students report COVID-19 symptoms and testing, regardless of test results, to University Health Services, and faculty and staff report to the HealthPoint Occupational Health Program (OHP) as soon as possible. Please see this link to understand what needs to be reported. In addition, to help understand what to do if a fellow student in the class (or the instructor or TA) tests positive for COVID, see this University Health Services link.


Note that this is a preliminary schedule that is subject to change.