Meeting Schedule

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Location: Hybrid - Muenzinger D430, and the zoom link below

Time: Wednesdays at 11:30am, Mountain Time

Zoom link: https://cuboulder.zoom.us/j/97014876908

Date Title
08/28/2024 Planning, introductions, welcome!
09/04/2024 Brunch Social
09/11/2024 Watch and discuss NLP keynote

Winner: Barbara Plank’s “Are LLMs Narrowing our Horizon? Let’s Embrace Variation in NLP!”

09/18/2024 CLASIC presentations
09/25/2024 Invited talks/discussions from Leeds and Anschutz folks: Liu Liu, Abe Handler, Yanjun Gao, Curry Guinn


10/02/2024 Martha Palmer, Annie Zaenen, Susan Brown, Alexis Cooper.

Title: Testing GPT4's interpretation of the Caused-Motion Construction

Abstract: The fields of Artificial Intelligence and Natural Language Processing have been revolutionized by the advent of Large Language Models such as GPT4. They are perceived as being language experts and there is a lot of speculation about how intelligent they are, with claims being made about “Sparks of General Artificial Intelligence.” This talk will describe in detail an English linguistic construction, the Caused Motion Construction, and compare prior interpretation approaches with current LLM interpretations. The prior approaches are based on VerbNet. It’s unique contributions to prior approaches will be outlined. Then the results of a recent preliminary study probing GPT4’s analysis of the same constructions will be presented. Not surprisingly, this analysis illustrates both strengths and weaknesses of GPT4’s ability to interpret Caused Motion Constructions and to generalize this interpretation.

Recording: https://o365coloradoedu-my.sharepoint.com/:v:/r/personal/mpalmer_colorado_edu/Documents/BoulderNLP-Palmer-Oct2-2024.mp4?csf=1&web=1&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJPbmVEcml2ZUZvckJ1c2luZXNzIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXciLCJyZWZlcnJhbFZpZXciOiJNeUZpbGVzTGlua0NvcHkifX0&e=aCHeN8


10/09/2024 NAACL Paper Clinic: Come get feedback on your submission drafts!
10/16/2024 Senior Thesis Proposals:


Alexandra Barry

Title: Benchmarking LLM Handling of Cross-Dialectal Spanish

Abstract: This proposal introduces current issues and gaps in cross-dialectal NLP in Spanish as well as the lack of resources available for Latin American dialects. The presentation will cover past work in dialect detection, translation, and benchmarking in order to build a foundation for a proposal that aims to create a benchmark that analyses LLM robustness across a series of tasks in different Spanish dialects


Tavin Turner

Title: Agreeing to Disagree: Statutory Relational Stance Modeling

Abstract: Policy division deeply affects which bills get passed in legislature, and how. So far, statutory NLP has predicted voting breakdowns, interpreted stakeholder benefit, informed legal decision support systems, and much more. In practice, legislation demands compromise and concession to pass important policy, yet models often struggle to reason over the whole act. Leveraging neuro-symbolic models, we seek to intermediate this challenge with relational structures of statutes’ sectional stances – modeling stance agreement, exception, etc. Beyond supporting downstream statutory analysis tasks, these structures could help stakeholders understand how a bill impacts them, litmus the cooperation within a legislature, and reveal patterns of compromise that aid a bill through ratification.

10/23/2024 Ananya Ganesh's PhD Dissertation Proposal

Title: Reliable Language Technology for Classroom Dialog Understanding

Abstract: In this proposal, I will lay out how NLP models can be developed to address realistic use cases in analyzing classroom dialogue. Towards this goal, I will first introduce a new task and corresponding dataset, focused on detecting off-task utterances in small-group discussions. I will then propose a method to solve this task that considers how the inherent structure in the dialog can be used to learn richer representations of the dialog context. Next, I will introduce preliminary work on applying LLMs in the in-context learning setting for a broad range of tasks pertaining to qualitative coding of classroom dialog, and discuss potential follow-up work. Finally, keeping in mind our goals of serving many independent stakeholders, I will propose a study to incorporate differing stake-holder’s subjective judgments while curating gold-standard data for classroom discourse analysis.

10/30/2024 Marie McGregor's area exam

Title: Adapting AMR Metrics to UMR Graphs

Abstract: Uniform Meaning Representation (UMR) expands on the capabilities of Abstract Meaning Representation (AMR) by supporting document-level annotation, suitability for low-resource languages, and support for logical inference. As a framework for any sort of representation is developed, a way to measure the similarities or differences between two representations must be developed in tandem to support the creation of parsers and for computing inner-annotator agreement (IAA). Fortunately, there exists robust research into metrics to assess the similarity of AMR graphs. The usefulness of these metrics to UMRs depends on four key aspects: scalability, correctness, interpretability, and cross-lingual suitability. This paper investigates the applicability of AMR metrics to UMR graphs along these aspects in order to create useful and reliable UMR metrics.

11/06/2024 Kevin Stowe - on Zoom
11/13/2024 Invited talk by Nick Dronen and Seminar Lunch
11/20/2024 Abteen's proposal
11/27/2024 No meeting: Fall break
12/04/2024 Enora's prelim
12/11/2024 DJ's prelim
1/23/25 Chenhao Tan CS Colloquium, 3:30pm


Past Schedules