HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification
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 cl...
Saved in:
Main Authors: | , , , , , , |
---|---|
Format: | Conference or Workshop Item |
Published: |
Association for Computational Linguistics
2023
|
Online Access: | http://scholars.utp.edu.my/id/eprint/38026/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175400718&partnerID=40&md5=50bd07eed227e02d3ae47e0fe7e50f81 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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. |
---|