Lea Kawaletz
2023
Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse
Regina Stodden
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Laura Kallmeyer
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Lea Kawaletz
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Heidrun Dorgeloh
Proceedings of the 10th Workshop on Argument Mining
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 state-of-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.
2022
Developing an argument annotation scheme based on a semantic classification of arguments
Lea Kawaletz
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Heidrun Dorgeloh
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Stefan Conrad
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Zeljko Bekcic
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises. Both argument detection and classification are complex discourse processing tasks. Our paper introduces a semantic classification of arguments that can help to facilitate argument detection. We report on our experiences with corpus annotations using a function-based classification of arguments and a procedure for operationalizing the scheme by using semantic templates.
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