Argument mining (AM), aiming to extract and identify argumentative structures from natural language text, has become an established field in the NLP community. 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 solve the problems, a research team led by Jiasheng SI published their new research on 15 January 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed 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.
Comprehensive experiments are conducted on the UKP ArgMin dataset in both in-domain and cross-domain scenarios to show the effectiveness of the proposed model.
Future work can explore the multi-word topic extraction in the topic and semantic joint training, and seek ways to combine the external knowledge and the topic information for more accurate argument mining.
DOI
10.1007/s11704-025-40460-y