TY - JOUR
T1 - Transfer Learning from Multilingual DeBERTa for Sexism Identification
AU - Ta, Hoang Thang
AU - Rahman, Abu Bakar Siddiqur
AU - Najjar, Lotfollah
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2022
Y1 - 2022
N2 - In this paper, we address the Task 1 and Task 2 of the EXIST 2022 in detecting sexism in a broad sense, from ideological inequality, sexual violence, misogyny to other expressions that involve implicit sexist behaviours in social networks. We apply transfer learning from a pre-trained multilingual DeBERTa (mDeBERTa) model and its zero classification to gain a better performance than BERT-based approaches. Lastly, we combine all 3 methods: mDeBERTa, zero classification, and BERT for majority vote. For Task 1, mDeBERTa is the best method with an accuracy of 76.09% and F1 of 76.08%. Meanwhile, an accuracy of 66.26% and F1 of 47.06% are the best results in Task2, when using majority vote. Our main contribution is to use DeBERTa and zero classification with designing only one classifier in sexism identification.
AB - In this paper, we address the Task 1 and Task 2 of the EXIST 2022 in detecting sexism in a broad sense, from ideological inequality, sexual violence, misogyny to other expressions that involve implicit sexist behaviours in social networks. We apply transfer learning from a pre-trained multilingual DeBERTa (mDeBERTa) model and its zero classification to gain a better performance than BERT-based approaches. Lastly, we combine all 3 methods: mDeBERTa, zero classification, and BERT for majority vote. For Task 1, mDeBERTa is the best method with an accuracy of 76.09% and F1 of 76.08%. Meanwhile, an accuracy of 66.26% and F1 of 47.06% are the best results in Task2, when using majority vote. Our main contribution is to use DeBERTa and zero classification with designing only one classifier in sexism identification.
KW - DeBERTa
KW - EXIST 2022
KW - IberLEF
KW - Offensive Language
KW - Sexism Identification
KW - Text Classification
UR - http://www.scopus.com/inward/record.url?scp=85137339044&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85137339044
SN - 1613-0073
VL - 3202
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2022 Iberian Languages Evaluation Forum, IberLEF 2022
Y2 - 20 September 2022
ER -