Multi-Task Attentive Residual Networks for Argument Mining

• URL: http://arxiv.org/abs/2102.12227v3
• Date: Thu, 25 May 2023 22:46:54 GMT
• Title: Multi-Task Attentive Residual Networks for Argument Mining
• Authors: Andrea Galassi, Marco Lippi, Paolo Torroni
• Abstract summary: We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble. We present an experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays.
• Score: 14.62200869391189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.

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