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

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 2190–2196 Barcelona, Spain (Online), December 12, 2020. 2190 TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings Rajalakshmi Sivanaiah, Angel Deborah S, S Milton Rajendram, Mirnalinee T T Department of Computer Science and Engineering SSN College of Engineering Chennai 603 110, Tamil Nadu, India rajalakshmis@ssn.edu.in, angeldeborahs@ssn.edu.in miltonrs@ssn.edu.in, mirnalineett@ssn.edu.in 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. 1 Introduction The usage of offensive or hate words is increasing these days, largely in online communication. People find it easier to express their opinions and thoughts in online sources rather than do it personally. The anonymity provided by the online environment encourages many people to express their views in aggressively. The information spread rate is extremely fast in online social media. People, without checking the validity of the information they receive, spread it to others. The text content posted in messages, websites, social media and blogs are highly unstructured, informal, often misspelt, and use shorthanded notations, emojis and emoticons. Getting the meaning in the natural text is a complicated task. There is ongoing research in the field of offensive language or hate speech detection, yet it remains only a goal to obtain a full-fledged model. We have participated in the offensive language task conducted in SemEval-2019 by Zampieri et al. (2019a) with various machine learning and deep learning models in Rajalakshmi et al. (2019a). For SemEval-2020 by Zampieri et al. (2020), we have developed and tested deep learning models with different word embeddings. We have used GloVe (Pennington et al., 2014), Word2Vec (Mikolov et al., 2015) and BERT embedding (Devlin et al., 2018) pretrained models to identify the presence of offensiveness in tweets. We have participated in subtask A (OFF/NOT) of classifying whether a tweet is offensive or non-offensive and subtask B (TIN/UNT) of classifying whether an offensive tweet is targeted to anyone or untargeted. The rest of the paper is organized as follows. Section 2 surveys the related work in this field. Section 3 describes the methodology used to solve the task. Results are discussed in section 4 and conclusion in section 5. 2 Related Work Recently, we have seen great strides in the research on profanity speech detection in social media, which includes hate speech detection, offensive language identification, and abusive language detection. Several workshops such as GermEval, SemEval, HatEval, and …

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