Bridging the Domain Gap for Stance Detection for the Zulu language

Gcinizwe Dlamini, Imad Eddine Ibrahim BEKKOUCH, Adil Khan, Leon Derczynski

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

Misinformation has become a major concern in recent last years given its spread across our information sources. In the past years, many NLP tasks have been introduced in this area, with some systems reaching good results on English language datasets. Existing AI based approaches for fighting misinformation in literature suggest automatic stance detection as an integral first step to success. Our paper aims at utilizing this progress made for English to transfers that knowledge into other languages, which is a non-trivial task due to the domain gap between English and the target languages. We propose a black-box non-intrusive method that utilizes techniques from Domain Adaptation to reduce the domain gap, without requiring any human expertise in the target language, by leveraging low-quality data in both a supervised and unsupervised manner. This allows us to rapidly achieve similar results for stance detection.
Original languageEnglish
Title of host publicationProceedings of the 2022 Intelligent Systems Conference (IntelliSys)
Publication date1 Sep 2022
Pages312-325
Publication statusPublished - 1 Sep 2022

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