Multi-Task Learning for Argumentation Mining in Low-Resource Settings
Table 2: Macro-F1 for AM component identification, comparing MTL, STL (significant differences in bold with p<0.01, p<0.05 if * using Mann-Whitney U Test) and union baseline (BL).
['Dataset', '21K', '12K', '6K', '1K']
We compare STL on each task, MTL with all remaining datasets as auxiliary tasks, and the union baseline. For three of the six datasets, MTL yields a significant improvement in all sparsity scenarios. Interestingly, these are the datasets with only one or two types of argument components. For the other three datasets, MTL only yields an improvement in the sparser data scenarios. The union baseline generally performs (considerably) worse than STL in all scenarios. This implies that the domains and component types (label spaces) used in the different AM datasets are too diverse to model them as one single task and that the improvement of MTL over STL cannot be attributed to more available data.