Corpus Wide Argument Mining - a Working Solution
Table 1: Accuracy of sentence classification over BlendNet.
['[BOLD] Arch.', '[BOLD] trained on', '[BOLD] Accuracy']
The BAMaskS network used here has the same architecture as that in \citeauthorshnarch-etal-2018-will \shortciteshnarch-etal-2018-will. It is therefore interesting to evaluate how the differences between their work and ours - greatly expanding the training data, considering the motion explicitly and using the BERT architecture - are reflected over the benchmark based on their data. Training BAMaskS on the VLD improves accuracy from 74% to 78%. In other words, even though BlendNet was trained on Wikipedia sentences, and the benchmark is also composed of Wikipedia sentences, the larger size of the VLD more than compensates for the change in domain. Shifting to the BERT architecture improves accuracy to 81%, even without adding the motion as part of the input. Finally, the best accuracy is attained when combining all three modification, for a total of nearly 14% improvement over the best results reported by \citeauthorshnarch-etal-2018-will \shortciteshnarch-etal-2018-will.