Topic-enhanced argument mining via mutual learning

Jiasheng Si, Yingjie Zhu, Rui Wang, Wenpeng Lu, Yulan He, Deyu Zhou

Abstract: Given a controversial target, such as “nuclear energy”, information-seeking argument mining aims to identify argumentative text from diverse sources. The main challenge in this task comes three-fold: the insufficiency of contextual information on targets, cross-domain adaptation across varying targets, and implicit argumentative information within the argument. Current approaches primarily address the first two challenges by improving the integration of target-related semantic information with arguments, while there has been little work on modeling all three aspects. To address these challenges, inspired by the potential capability of the neural topic model for mining the local and global topic information contained in the dataset, we propose a novel topic-enhanced information-seeking argument mining approach by leveraging the mutual interaction between the neural topic model and the language model. Specifically, (i) the global topic information is extracted from the corpora to encapsulate the common knowledge across different targets for solving the cross-domain adaptation; (ii) to capture the contextual information on targets, the target is augmented by target-aware subtopics derived from the global topic-word distribution; (iii) to capture the implicit argumentative information within the argument, the local topic information is captured by minimizing the similarity between its local topic distribution and its semantic representation through mutual learning. Experimental results show the superiority of the proposed model compared to the state-of-the-art baselines in both in-domain and cross-domain scenarios.

External IDs:dblp:journals/fcsc/SiZWLHZ26