Tribrid: Stance Classification with Neural Inconsistency Detection

Song Yang, Jacopo Urbani

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)
PublisherAssociation for Computational Linguistics (ACL)
Pages6831-6843
Number of pages13
ISBN (Electronic)9781955917094
Publication statusPublished - Nov 2021
Event2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
Country/TerritoryDominican Republic
CityVirtual, Punta Cana
Period7/11/2111/11/21

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics

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