Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection

Anushka Prakash, Harish Tayyar Madabushi

Research output: Chapter in Book/Report/Conference proceedingChapter in a published conference proceeding

Abstract

The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate. Unfortunately, this same ease of accessing and sharing information has led to an explosion of misinformation and propaganda. Given that stance detection can significantly aid in veracity prediction, this work focuses on boosting automated stance detection, a task on which pre-trained models have been extremely successful on, as on several other tasks. This work shows that the task of stance detection can benefit from feature based information, especially on certain under performing classes, however, integrating such features into pre-trained models using ensembling is challenging. We propose a novel architecture for integrating features with pre-trained models that address these challenges and test our method on the RumourEval 2019 dataset. This method achieves state-of-the-art results with an F1-score of 63.94 on the test set.
Original languageEnglish
Title of host publicationProceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Place of PublicationBarcelona, Spain (Online)
PublisherInternational Committee on Computational Linguistics (ICCL)
Pages22-32
Number of pages11
Publication statusPublished - 1 Dec 2020

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