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.
Argument Mining with Structured SVMs and RNNs
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abstract
We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.
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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.