TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings

Rajalakshmi Sivanaiah, Angel Suseelan, S Milton Rajendram, Mirnalinee T.t.


Abstract
This paper describes the work of identifying the presence of offensive language in social media posts and categorizing a post as targeted to a particular person or not. The work developed by team TECHSSN for solving the Multilingual Offensive Language Identification in Social Media (Task 12) in SemEval-2020 involves the use of deep learning models with BERT embeddings. The dataset is preprocessed and given to a Bidirectional Encoder Representations from Transformers (BERT) model with pretrained weight vectors. The model is retrained and the weights are learned for the offensive language dataset. We have developed a system with the English language dataset. The results are better when compared to the model we developed in SemEval-2019 Task6.
Anthology ID:
2020.semeval-1.291
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
2190–2196
Language:
URL:
https://aclanthology.org/2020.semeval-1.291
DOI:
10.18653/v1/2020.semeval-1.291
Bibkey:
Cite (ACL):
Rajalakshmi Sivanaiah, Angel Suseelan, S Milton Rajendram, and Mirnalinee T.t.. 2020. TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2190–2196, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings (Sivanaiah et al., SemEval 2020)
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PDF:
https://preview.aclanthology.org/update-css-js/2020.semeval-1.291.pdf