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Computer Science > Computation and Language

arXiv:2109.05074 (cs)
[Submitted on 10 Sep 2021]

Title:FBERT: A Neural Transformer for Identifying Offensive Content

Authors:Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe, Alexander Ororbia
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Abstract:Transformer-based models such as BERT, XLNET, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media. In this paper, we present fBERT, a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over $1.4$ million offensive instances. We evaluate fBERT's performance on identifying offensive content on multiple English datasets and we test several thresholds for selecting instances from SOLID. The fBERT model will be made freely available to the community.
Comments: Accepted to EMNLP Findings
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
Cite as: arXiv:2109.05074 [cs.CL]
  (or arXiv:2109.05074v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2109.05074

Submission history

From: Tharindu Ranasinghe Mr [view email]
[v1] Fri, 10 Sep 2021 19:19:26 UTC (167 KB)
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