Weakly-supervised Argument Mining with Boundary Refinement and Relation Denoising
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 weakly-labeled 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 IoU75 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.
Paper Type: Long
Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Research Area Keywords: Argument Mining, Applications, Weak Supervision
Contribution Types: NLP engineering experiment, Approaches to low-resource settings
Languages Studied: English
Submission Number: 2643
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