Zero-shot learning for (dis)agreement detection in meeting trancripts

Zero-shot learning for (dis)agreement detection in meeting trancripts: Comparing latent topic models and large language models

de Weerd, Daniël (TU Delft Electrical Engineering, Mathematics and Computer Science)

Tarvirdians, M. (mentor)
Jonker, C.M. (mentor)
Molenaar, M.L. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-07-03

This paper presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases in the text as being either in favor of, against or expressing no opinion on a given thesis. To identify relevant theses, we compare the performance of a latent Dirichlet allocation-based topic model against that of a diverse set of large language models. To classify the stance of a phrase with respect to a thesis, only large language models are used. We find that, while state-of-the-art large language models do not outperform topic modeling-based approaches in extracting semantically relevant content, they are capable of presenting such content in a more concise and grammatically correct manner. We also find that state-of-the-art large language models are not capable of accurately performing stance classification as described above.

summarization
large language models
meeting transcript

http://resolver.tudelft.nl/uuid:01189347-da5f-45ac-b4d9-7e3fc18d7802

Student theses

bachelor thesis

© 2023 Daniël de Weerd