Overview of EXIST 2023 – Learning with Disagreement for Sexism Identification and Characterization

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Abstract

In recent years, the rapid increase in the dissemination of offensive and discriminatory material aimed at women through social media platforms has emerged as a significant concern. This trend has had adverse effects on women’s well-being and their ability to freely express themselves. The EXIST campaign has been promoting research in online sexism detection and categorization since 2021. The third edition of EXIST, hosted at the CLEF 2023 conference, consists of three tasks, two of which are the continuation of EXIST 2022 (sexism identification and sexism categorization), and a third and novel one is on source intention identification. For this edition, new test and training data are provided and the “learning with disagreement” paradigm is adopted to address disagreements in the labelling process and promote the development of equitable systems that are able to learn from different perspectives on the sexism phenomena. 28 teams participated in the three EXIST 2023 tasks, submitting 232 runs. This lab overview describes the tasks, dataset, evaluation methodology, approaches and results.

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Plaza, L., Carrillo-de-Albornoz, J., Morante, R., Amigó, E., Gonzalo, J., Spina, D., & Rosso, P. (2023). Overview of EXIST 2023 – Learning with Disagreement for Sexism Identification and Characterization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14163 LNCS, pp. 316–342). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-42448-9_23

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