LIN 389C: Research in Computational Linguistics


LIN389C is a research course for students who work in computational linguistics. It is aimed at students with advanced knowledge in natural language processing and machine learning techniques who are doing research in the area. In the course, we discuss current research by course participants, review foundational knowledge that is relevant to participants' research, and talk about big-picture issues and current research in field.


Course information: 

Course Purpose

To teach about, encourage, and give students time for research. Also to encourage discussion and collaboration among students interested in the same subfield.

Course Organization

There are six constituencies who will not be treated exactly equally in the course because their needs are different:

Course Components

The course consists of two main parts: a research seminar, and discussion of ongoing student research.


Grading policy

Grading will be based on the course requirement listed above.

This course does not have a final exam or midterm exam.

The course will use plus-minus grading, using the following scale:

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.

Classroom safety and Covid-19

To help preserve our in person learning environment, the university recommends the following.

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, 5121-471-6259.

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.

Emergency Evacuation Policy

Occupants of buildings on The University of Texas at Austin campus are required to evacuate buildings when a fire alarm is activated. Alarm activation or announcement requires exiting and assembling outside. Familiarize yourself with all exit doors of each classroom and building you may occupy. Remember that the nearest exit door may not be the one you used when entering the building. Students requiring assistance in evacuation shall inform their instructor in writing during the first week of class. In the event of an evacuation, follow the instruction of faculty or class instructors. Do not re-enter a building unless given instructions by the following: Austin Fire Department, The University of Texas at Austin Police Department, or Fire Prevention Services office. Information regarding emergency evacuation routes and emergency procedures can be found at

Behavior Concerns Advice Line (BCAL)

If you are worried about someone who is acting differently, you may use the Behavior Concerns Advice Line to discuss by phone your concerns about another individual's behavior. This service is provided through a partnership among the Office of the Dean of Students, the Counseling and Mental Health Center (CMHC), the Employee Assistance Program (EAP), and The University of Texas Police Department (UTPD). Call 512-232-5050 or visit

Adapting the class format to deal with the ongoing pandemic

Here is the plan as of August 23, 2021:


In the first week, we will talk about topics to cover in this semester's class. Please bring suggestions for topics that are relevant to your research. A collection of topics suggested at the end of the previous semester is listed under Topics below.

Week 1:  Aug 25

We talk in class about topics you would like to see covered in class. Please bring suggestions!

We also do a round-table in which we talk about research done over the summer.

Week 2:  Sep 1: Machine learning and language models

We are reading: Schick and Schütze, It's not just size that matters: small language models are also few-shot learners. 

Venkat is leading the discussion.

Week 3:  Sep 8: Machine learning and language models

Aghajanyan, Zettlemoyer and Gupta, Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Hongli will lead the discussion.

Also, see below for some introductory material on deep learning. 

Week 4:  Sep 15: Machine learning and language models

Transformers and Hopfield networks:

Gabriella will lead the discussion.

Week 5: Sep 22: Machine learning and language models

We are reading Zhang, van de Meent and Wallace, Disentangling Representations of Text by Masking Transformers, a recent preprint. 

Nafal is leading the discussion.

Week 6: Sep 29: Social computational linguistics

PNAS: cognitive distortions on the rise -- or maybe not? with response by Kyle

Venkat is leading the discussion.

Week 7: Oct 6: Social computational linguistics

We are reading: Seraj, Blackburn, and Pennebaker: Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup,

Hongli is leading the discussion.

Week 8: Oct 13: Social computational linguistics, computational linguistics and society

Foundation models: We are reading parts of "On the opportunities and risks of foundation models",

We are reading:

We are not reading the whole thing, which is 212 pages!

Yejin is leading the discussion.

Week 9: Oct 20: Prompt engineering

Survey paper Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,

We are reading sections 1-5 and 10. 

Venkat is leading the discussion.

Week 10: Oct 27: Prompt engineering

Asking the right questions: Brenden Lake and colleagues, 

And linking this to Jessy’s work on question generation

Week 11: Nov 3: Narratives

Narrative Theory for Computational Narrative Understanding: Piper et al. (EMNLP 2021),

Yejin is leading the discussion.

Week 12: Nov 10: Readability

Linguistic features for readability assessment (Deutsch, Jasbi, Shieber):

Nafal is leading the discussion.

Week 13: Nov 17: Narratives

It’s not Rocket Science: Interpreting Figurative Language in Narratives: Chakrabarty et al. (ArXiv 2021), ttps://

Venkat is leading  the discussion.

Week 14: No class, Thanksgiving break

Week 15: Dec 1: Emotions and Narratives

Lee et al. NAACL 2021, "Modeling Human Mental States with an Entity-based Narrative Graph"

Hongli is leading the discussion.

Final paper due date: Thursday December 9, end of day .

Introductory material on deep learning

Michael Nielsen's book Neural Networks and Deep Learning gives a very nice and accessible introduction to deep learning.

I also like the relevant chapters from the upcoming 3rd edition of the Jurafsky and Martin book:

Jay Alammar has some very nice illustrations of key ideas in neural models:

Machine learning:

Machine learning, language and cognition:

Computational creativity


Social computational linguistics:

Metaphor processing:

Discourse and pragmatics

Ethical AI