Course Information

• Course: LIN 389C, Research in Computational Linguistics, 40480
• Semester: Spring 2021
• Webpage: http://jessyli.com/courses/lin389c
• Meeting: W 12-3, on Zoom (link on Canvas)

Contact information

• Course organizer: Jessy Li; email: jessy@austin.utexas.edu
  • Office hours: T/Th 11-12, W 3-4, on Zoom (link on Canvas)

Course Purpose

To teach about, encourage, and give students time for research. Also to establish vertical cohorts 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 (QP), 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. NOTE that this course is designed to provide support and necessary foundations to research in Computational Linguistics, rather than an overview of research topics. You are expected to carry out one research project throughout the semester.

Course Components

• Suggested topic list
• Research seminar: This semester we focus on a recent papers in computational linguistics. Topics and readings will be given under schedule. Typically one of the students in the class will be responsible for giving a short initial summary of the paper and for preparing some questions to get the discussion going.
• 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, submit literature discussion at end of semester.
  Second semester, submit first-year paper draft halfway through the semester, 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, submit research discussion paper at end of semester.
  Second semester, submit second-year paper draft halfway through the semester, 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, submit QP progress report at end of semester.
  Second semester, submit QP draft halfway through the semester, 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).

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.

Topics

This is the list of suggested topics we will discuss this semester (to be refined):

• Understanding explainability
• Event understanding and temporal reasoning
• NLP and social science
• Graph neural networks

Schedule

Each week, unless noted otherwise, we use the first part of the class to discuss the topic of the week, and we use the second part for a round-table discussing people’s research.

• Week 1, 1/20
  • Plan for the semester: Which topics to focus on, who gives talks where.
  • Extended round table: Research results and research/publication plans for the spring.
• Week 2, 1/27
  • We will read Jacovi and Goldberg, Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?. Venkat will lead the discussion.
• Week 3, 2/3
  • We will read Hase and Bansal, Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?. Jiyang will lead the discussion.
  • Gabriella will do a practice talk.
• Week 4, 2/10
  • We will read Pruthi et al., Learning to Deceive with Attention-Based Explanations. Anurag will lead the discussion.
• Week 5, 2/17
  • Class canceled; stay safe!
• Week 6, 2/24
  • We will read Webson et al., Are “Undocumented Workers” the Same as “Illegal Aliens”? Disentangling Denotation and Connotation in Vector Spaces. Gabriella will lead the discussion.
• Week 7, 3/3
  • We will read Pryzant et al., Automatically Neutralizing Subjective Bias in Text. Venkat will ldead the discussion.
• Week 8, 3/10
  • We will read Tian et al., Scene Restoring for Narrative Machine Reading Comprehension. Yejin will lead the discussion.
• Midterm papers due: midnight 3/14
• Week 9, 3/17 (Spring break)
• Week 10, 3/24
  • We will read Vashishtha, Van Durme and White, Fine-Grained Temporal Relation Extraction, Kapil will lead the discussion. We will also discuss their newer work Temporal Reasoning in Natural Language Inference though not in detail.
• Week 11, 3/31
  • We will read Han et al., DEER: A Data Efficient Language Model for Event Temporal Reasoning. Venkat will lead the discussion.
• Week 12, 4/7
  • We will read Camacho-Collados et al, Relational Word Embeddings. Gabriella will lead the discussion.
• Week 13, 4/14
  • We will read Bouraoui et al., Inducing Relational Knowledge from BERT. Gabriella will lead the discussion.
• Week 14, 4/21
  • We will read Spiliopoulou et al., Definition Frames: Using Definitions for Hybrid Concept Representations. Juan Diego will lead the discussion.
• Week 15, 4/28
  • We will read Garcia et al., Probing for idiomaticity in vector space models. Kapil will lead the discussion.
• Week 16, 5/5
  • Extended roundtable and plans for next semester.
• Final paper deadlines:
  • QP/2nd year papers for Linguistics students: midnight 5/5
  • Project reports for all others: midnight 5/17