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Multi-Task Learning for Argumentation Mining in Low-Resource Settings
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We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.
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Cited by 1 Pith paper
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AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation
A single LoRA fine-tuned Llama-3.1-8B-Instruct model trained jointly on eight argument-mining tasks across 19 datasets matches or beats task-specific models, and merged models offer a cheaper compromise.
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