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Tribrid: Stance Classification with Neural Inconsistency Detection
[article]
2021
arXiv
pre-print
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
arXiv:2109.06508v1
fatcat:pq446e4w3ncrlbbbnftrs3umma