HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification

Aliyu, S.M. and Abdulmumin, I. and Muhammad, S.H. and Ahmad, I.S. and Salahudeen, S.A. and Yusuf, A. and Lawan, F.I. (2023) HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification. In: UNSPECIFIED.

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Abstract

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052 F1-score. © 2023 Association for Computational Linguistics.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 0; Conference of 17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 ; Conference Date: 13 July 2023 Through 14 July 2023; Conference Code:192857
Uncontrolled Keywords: Computational linguistics; Semantics; Transfer learning, Data informations; F1 scores; Language model; Multi-level classifications; Multilevels; Offensive languages; Sentiment classification; Side information; Synthetic data; Transfer learning, Classification (of information)
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 11 Dec 2023 03:01
Last Modified: 11 Dec 2023 03:01
URI: http://scholars.utp.edu.my/id/eprint/38026

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