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.
Here is a list of topics we collected at the end of Fall 2022. There will be an opportunity to add to this list and select from it
Probing, especially counterfactual / causal
Extracting training data from GPT-3
Scripts/schemas/stories
Affective dialog generation + appraisal theory + maybe persuasion
Language models as a semantic/syntactic theory
Computational modeling of constructions
Emotions and discourse structure
Question Under Discussion
A more detailed list is in a Google doc, which we'll look at in the first class session.Â
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 above.
Week 1: Jan 11
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: Â Jan 18: LLMs and human judgments for syntax, semantics, discourse
Reading: Arehalli, Dillon, and Linzen: Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities
We'll all prepare: 2 things we like about the paper, 2 things we didn't like, and 2 things we didn't understand
We also resume our discussion of topics for the semester, so please think about topics you would like to see covered
Week 3: Jan 25: LLMs and human judgments for syntax, semantics, discourse
Reading: Antonello and Huth: Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data
Week 4: Feb 1: Ice Day
Week 5: Feb 8: ChatGPT
Readings:Â
Feel free to pre-post your questions for discussion on the Slack channel
Week 6: Feb 15: Construction Grammar
Reading: Ludovica Pannitto and AurĂ©lie Herbelot, Recurrent babbling: evaluating the acquisition of grammar from limited input data.Â
For background reading, here is a review article by Adele Goldberg on construction grammar.
Week 7: Feb 22: Construction Grammar
We are reading: Weissweiler et al, The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative
Leonie Weissweiler will join us for the discussion!
Week 8: Mar 1: Scripts/Situations/Scenarios/Generalized Event Knowledge
We are reading: DREAM: Improving Situational QA by First Elaborating the Situation (Gu et al., NAACL 2022). https://aclanthology.org/2022.naacl-main.82.pdf
Week 9: Mar 8: Scripts/Situations/Scenarios/Generalized Event Knowledge
We are reading: Koupaee, Durrett, Chambers, and Balasubramanian, Modeling Complex Event Scenarios via Simple Entity-focused Questions
Spring break: week of Mar 15
Week 10: Mar 22: Emotions
Desmond Ong comes to visit! He is giving a talk, and he'll do an Ask Me Anything about emotions. So, let's bring questions.
Week 11: Mar 29: Question under Discussion
We are reading: Anton Benz & Katja Jasinskaja, Questions under Discussion: From Sentence to Discourse.
Hongli gives a research update.
Week 12: April 5
Jamie Pennebaker comes to visit. We are reading: https://journals.sagepub.com/doi/full/10.1177/0261927X20967028, Natural language analysis and the psychology of verbal behavior.
Yating gives a research update.
Week 13: April 12: Question under Discussion
Yejin does a dry run of her prospectus.
Week 14: April 19
Gabriella may do a dry run of her prospectus.
We talk about babyLM.
We also talk about topics for next semester.
Final paper due date: TBA.
Course information:
LIN 389C Research in Computational Linguistics. Unique number: 39955
Spring 2023
Course time: Wednesdays 12-3.
Course location: Computational linguistics lab, RLP 4.422
Course on Canvas: https://utexas.instructure.com/courses/1359541
Course organizer: Katrin Erk ,katrin DOT erk AT utexas DOT edu
Office hours: Monday 1-2 on zoom (see Canvas), Tuesday 1:30-3:30 RLP 4.734
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:
first-year students: need general context and help on first research projects and first-year papers
second-year students: need feedback on the research they have done, prepare for further research, and write second-year papers
third-year students: need to write their Qualifying Papers, write grant proposals
post-candidacy pre-proposal students: need to write and present their dissertation proposals
post-candidacy dissertation students: need to write dissertations, get feedback
students from other departments: needs will vary
Course Components
The course consists of two main parts: a research seminar, and discussion of ongoing student research.
Research seminar: Topics and readings will be given under Schedule.
Format: Each student prepares 3 bullets about what you like about the paper, 3 bullets about what you don’t, plus questions/other comments
We'll randomize the order in which people get to discuss their bullet poinst
Discussion of ongoing student research:
Round-table: short presentations by all participants about their current research. This will happen almost every week.
On-going research: longer presentations (30 minutes or an hour including discussion), students, faculty, auditors if they wish.
Dissertation proposal presentations.
Dissertation progress talks.
Practice talks for conference presentations.
Requirements
First-year students: Attend all classes/activities. Talk about research.
First semester, submit literature discussion sketch halfway through the semester (see schedule), submit literature discussion at end of semester.
Second semester, submit first-year paper draft halfway through the semester (see schedule), submit first-year paper at end of semester.
Second-year students: Attend all classes/activities. Talk about research.
First semester, submit research discussion draft halfway through the semester (see schedule), submit research discussion paper at end of semester.
Second semester, submit second-year paper draft halfway through the semester (see schedule), submit second-year paper at end of semester.
Third-year students: Attend all classes/activities. Talk about research.
First semester, submit QP proposal halfway through the semester (see schedule), submit QP progress report at end of semester.
Second semester, submit QP draft halfway through the semester (see schedule), submit QP at end of semester.
Post-candidacy, pre-proposal students: Attend all presentations. Talk about research.
Dissertation-writing students: Attend all presentations, give at least one presentation during semester on doctoral research).
Students from other departments: a course project, with 2 documents: intermediate report (2-3 pages), final report (8 pages), deadlines as given in the schedule.
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:
A >= 93%
A- >= 90%
B+ >= 87%
B >= 83%
B- >= 80%
C+ >= 77%
C >= 73%
C- >= 70%
D+ >= 67%
D >= 63%
D- >= 60%
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.
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