Experiments using our approach reduced the gap between the F1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0. 32-5. 3 percent degradation.
no code implementations • 3 Nov 2020 • Ehud Aharoni, Allon Adir, Moran Baruch, Nir Drucker, Gilad Ezov, Ariel Farkash, Lev Greenberg, Ramy Masalha, Guy Moshkowich, Dov Murik, Hayim Shaul, Omri Soceanu
We present a simple and intuitive framework that abstracts the packing decision for the user.
Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory.
In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments.
We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset.