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

AbstractThis 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.