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)
- Venue:
- SemEval
- SIG:
- SIGLEX
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.semeval-1.291.pdf