Jahan, M. S., Beddiar, D. R., Oussalah, M., Arhab, N., & Bounab, Y. (2021). Hate and Offensive language detection using BERT for English Subtask A. In P. Mehta, T. Mandl, P. Majumder, & Mandar M. (Eds.), Working Notes of FIRE 2021 - Forum for Information Retrieval Evaluation, Gandhinagar, India, December 13-17, 2021 (pp. 262-272). RWTH Aachen University. http://ceur-ws.org/Vol-3159/T1-27.pdf
Hate and offensive language detection using BERT for English subtask A
|Author:||Jahan, Md Saroar1; Beddiar, Djamila Romaissa1; Oussalah, Mourad1;|
1University of Oulu, Faculty of Information Tech., CMVS, PO Box 4500, Oulu 90014, FINLAND
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022070551079
RWTH Aachen University,
|Publish Date:|| 2022-07-05
This paper presents the results and main findings of the HASOC-2021 Hate/Offensive Language Identification Subtask A. The work consisted of fine-tuning pre-trained transformer networks such as BERT and an ensemble of different models, including CNN and BERT. We have used the HASOC-2021 English 3.8k annotated twitter dataset. We compare current pre-trained transformer networks with and without Masked-Language-Modelling (MLM) fine-tuning on their performance for offensive language detection. Among different BERT MLM fine-tuned BERT-base, BERT-large, and ALBERT outperformed other models; however, BERT and CNN ensemble classifier that applies majority voting outperformed other models, achieving 85.1% F1 score on both hate/non-hate labels. Our final submission achieved 77.0 F1 in the HASOC-2021 competition.
CEUR workshop proceedings
|Pages:||262 - 272|
Working Notes of FIRE 2021 - Forum for Information Retrieval Evaluation, Gandhinagar, India, December 13-17, 2021
|Host publication editor:||
Forum for Information Retrieval Evaluation
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This project was partially funded by EU Project WaterLine (Downscaling Remotely Sensed Products to Improve Hydrological Modelling Performance), and EU Project YoungRes (#823701), which are gratefully acknowledged.
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