Multi-Task Attentive Residual Networks for Argument Mining

Andrea Galassi; Marco Lippi; Paolo Torroni

doi:10.1109/taslp.2023.3275040

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arXiv · 2102.12227

Multi-Task Attentive Residual Networks for Argument Mining

Andrea Galassi·Marco Lippi·Paolo Torroni

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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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Andrea Galassi, Marco Lippi, Paolo Torroni. 2021-02-24. Multi-Task Attentive Residual Networks for Argument Mining. https://doi.org/10.1109/taslp.2023.3275040

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