IIITG-ADBU at SemEval-2020 Task 12: Comparison of BERT and BiLSTM in Detecting Offensive Language

Arup Baruah, Kaushik Das, Ferdous Barbhuiya, Kuntal Dey


Abstract
Task 12 of SemEval 2020 consisted of 3 subtasks, namely offensive language identification (Subtask A), categorization of offense type (Subtask B), and offense target identification (Subtask C). This paper presents the results our classifiers obtained for the English language in the 3 subtasks. The classifiers used by us were BERT and BiLSTM. On the test set, our BERT classifier obtained macro F1 score of 0.90707 for subtask A, and 0.65279 for subtask B. The BiLSTM classifier obtained macro F1 score of 0.57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers. We conjecture that the presence of few misleading instances in the dataset is affecting the performance of the classifiers. Our analysis also discusses the need of temporal context and world knowledge to determine the offensiveness of few comments.
Anthology ID:
2020.semeval-1.204
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1562–1568
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.204
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.204.pdf