External Knowledge-Driven Argument Mining: Leveraging Attention-Enhanced Multi-Network Models
Abstract: Argument mining (AM) involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). The essence of ARs among ADUs is context-dependent and lies in maintaining a coherent flow of ideas, often centered around the relations between discussed entities, topics, themes or concepts. However, these relations are not always explicitly stated; rather, inferred from implicit chains of reasoning connecting the concepts addressed in the ADUs. While humans can infer such background knowledge, machines face challenges when contextual cues are not explicitly provided. This paper leverages external resources, including WordNet, ConceptNet, and Wikipedia to identify chain of semantic relations (knowledge paths) connecting the concepts discussed in the ADUs to obtain the implicit chains of reasoning. To effectively leverage these paths for AR prediction, we propose attention-based Multi-Network architectures. Various configurations of the architecture are evaluated on the external resources, and the configuration using Wikipedia achieves a new state-of-the-art performance with F-scores of 0.85, 0.84, 0.70, and 87, respectively, on four diverse datasets.
Paper Type: Long
Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Research Area Keywords: argument mining
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 381
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