Year: 2026
Type: conference-paper
Abstract: Argument mining (AM) involves extracting argument components and predicting relations between them to create argumentative graphs, which are essential for applications requiring argumentative comprehension.To automatically provide high-quality graphs, previous works require a large amount of human-annotated training samples to train AM models.Instead, we leverage a large language model (LLM) to assign pseudo-labels to training samples for reducing reliance on human-annotated training data.However, the training data weaklylabeled by the LLM are too noisy to develop an AM model with reliable performance.In this paper, to improve the model performance, we propose a center-based component detector that refines the boundaries of the detected components and a relation denoiser to deal with noise present in the pseudo-labels when classifying relations between detected components.Experimentally, our AM model improves the boundary detection obtained from the LLM by up to 16% in terms of IoU 75 and of the relation classification obtained from the LLM by up to 12% in terms of macro-F1 score.Our AM model achieves new state-of-the-art performance in weakly-supervised AM, showing up to a 6% improvement over the state-of-the-art component detector and up to a 7% improvement over the state-of-the-art relation classifier.Additionally, our model uses less than 20% of human-annotated data to match the performance of state-of-the-art fully-supervised AM models.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: Vlaams Supercomputer Centrum, CHIST-ERA
OpenAlex ID: W7140074205