Joint Learning for Efficient German Argument Mining

We propose a lightweight and adaptable model tailored to German argument mining in two dissimilar domains. It offers improvements over the state-of-the-art, as well as over recent LLM approaches. Specifically, we achieve an 11.5 % absolute improvement across all classes of the SPWSLE dataset as well as 3.5% absolute detection performance boost across all datasets of the CIMT collection. We show that for both tasks, the hitherto disjoint steps of argument detection followed by argument classification can be merged with no loss in quality, resulting in a greater than $2 x$ training and inference speed-up. For the naive baseline, this joint scheme also significantly improves results. For CIMT we propose realistic evaluation that incorporates actual detection results instead of simulating perfect detection. We also investigate the transferability between and to new datasets and find it to be higher than in previous work and in certain cases even better than within the dataset. A super model trained on all other datasets adapts to unseen data without re-training or performance loss.