CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining

ACL ARR 2024 December Submission282 Authors

Abstract: Argument Mining (AM) involves the automatic identification of argument structure in natural language. Traditional AM methods rely on micro-structural features derived from the internal properties of individual Argumentative Discourse Units (ADUs). However, argument structure is shaped by a macro-structure capturing the functional interdependence among ADUs. This macro-structure consists of segments, where each segment contains ADUs that fulfill specific roles to maintain coherence within the segment ($\textbf{local coherence}$) and across segments ($\textbf{global coherence}$). This paper presents an approach that models macro-structure, capturing both local and global coherence to identify argument structures. Experiments on heterogeneous datasets demonstrate superior performance in both in-dataset and cross-dataset evaluations. The cross-dataset evaluation shows that macro-structure enhances transferability to unseen datasets.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: Macro-structure, Argument Mining

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 282