Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs
(b) Claim-premise model.
['[BOLD] Language', '[ITALIC] A', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
We observed that the ADU classification reached highly varying results between the two datasets. We assume that the limited quantity of training data in the ReCAP dataset is the main reason for the variation. On the more than four times larger essay data we obtained an accuracy score of 0.80 which yields a strong indication of the model’s generalization ability. The claim-premise classification unfortunately did not meet expectations on neither the persuasive essays nor on the ReCAP data. We explain the difference in predictive power on both datasets due to the fact that the structure of the ReCAP dataset is too dissimilar to the PE18 dataset on which the models are trained on.