Tribrid: Stance Classification with Neural Inconsistency Detection

Song Yang, Jacopo Urbani


Abstract
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.
Anthology ID:
2021.emnlp-main.547
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6831–6843
Language:
URL:
https://aclanthology.org/2021.emnlp-main.547
DOI:
10.18653/v1/2021.emnlp-main.547
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.547.pdf
Software:
 2021.emnlp-main.547.Software.zip
Code
 karmaresearch/tribrid
Data
Perspectrum