Argument Mining with Structured SVMs and RNNs
Table 2: Test set F1 scores for link and proposition classification, as well as their average, on the two datasets. The number of test instances is shown in parentheses; best scores on overall tasks are in bold.
['[EMPTY]', 'Baseline SVM', 'Baseline SVM', 'Baseline SVM', 'Baseline RNN', 'Baseline RNN', 'Baseline RNN', 'Structured SVM', 'Structured SVM', 'Structured SVM', 'Structured RNN', 'Structured RNN', 'Structured RNN']
Following \newcitestabcoli, we compute F1 scores at proposition and link level, and also report their average as a summary of overall performance. The overall trend is that training using a structured objective is better than the baseline models, even when structured inference is applied on the baseline predictions. On UKP, for link prediction, the linear baseline can reach good performance when using inference, similar to the approach of \newcitestabcoli, but the improvement in proposition prediction leads to higher overall F1 for the structured models. Meanwhile, on the more difficult CDCP setting, performing inference on the baseline output is not competitive.