  We study the problem of performing automatic stance classification on social
media with neural architectures such as BERT. Although these architectures
deliver impressive results, their level is not yet comparable to the one of
humans and they might produce errors that have a significant impact on the
downstream task (e.g., fact-checking). To improve the performance, we present a
new neural architecture where the input also includes automatically generated
negated perspectives over a given claim. The model is jointly learned to make
simultaneously multiple predictions, which can be used either to improve the
classification of the original perspective or to filter out doubtful
predictions. In the first case, we propose a weakly supervised method for
combining the predictions into a final one. In the second case, we show that
using the confidence scores to remove doubtful predictions allows our method to
achieve human-like performance over the retained information, which is still a
sizable part of the original input.
