Work

Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse

Year: 2023

Type: conference-paper

Abstract: This paper introduces an approach which operationalizes the role of discourse connectives for detecting argument stance.Specifically, the study investigates the utility of masked language model probabilities of discourse connectives inserted between a claim and a premise that supports or attacks it.The research focuses on a range of connectives known to signal support or attack, such as because, but, so, or although.By employing a LightGBM classifier, the study reveals promising results in stance detection in English discourse.While the proposed system does not aim to outperform stateof-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.

Source: Workshop on Argument Mining (ArgMining)

DOI: 10.18653/v1/2023.argmining-1.2

Authors: Regina Stodden, Laura Kallmeyer, Lea Kawaletz, Heidrun Dorgeloh

Institution: Heinrich Heine University Düsseldorf

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Nvidia

OpenAlex ID: W4389518190