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

SEMEVAL 2020 · Rajalakshmi Sivanaiah, Angel Suseelan, S Milton Rajendram, Mirnalinee T.t. ·

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.

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Language Identification

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Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay • WordPiece

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

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