Argument Structure Mining Based on Effective Usage of Contextual Information

• Menglong Xu,
• Yanliang Zhang,
• Yapu Yang,

Vol. 19, No. 1, January 31, 2025
10.3837/tiis.2025.01.001, Download Paper (Free):

• Argument structure extraction
• contextual information
• Discourse markers
• Masked Language Modeling
• data augmentation

Abstract

Argument Structure Extraction (ASE) is increasingly prominent for its role in identifying discourse structure within documents. Many pioneering works have demonstrated that the contextual information in the document is vital for the final performance of ASE. Traditional context-aware methods relying on concatenation of contextual sentences have proven insufficient, introducing noise, inefficiency, and bias due to the reliance on discourse markers. To overcome these issues, we introduce Efficient Argument Structure Extraction (E-ASE), which eschews sentence concatenation in favor of encoding sentences separately and applying sentence-level attention to integrate context. To mitigate discourse marker bias, E-ASE employs a novel data augmentation technique, substituting discourse markers with a [MASK] token and leveraging Masked Language Modeling (MLM) loss. Our empirical research, conducted across five diverse datasets, demonstrates E-ASE's state-of-the-art (SOTA) performance, save for on the ECHR dataset, marking a significant advancement in the field of ASE by optimizing contextual information usage and enhancing both the training and inference processes.

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