Garain at SemEval-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1953–1960 Barcelona, Spain (Online), December 12, 2020. 1953 Garain at SemEval-2020 Task 12: Sequence based Deep Learning for Categorizing Offensive Language in Social Media Avishek Garain Computer Science and Engineering Jadavpur University Kolkata, India avishekgarain@gmail.com Abstract SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). The task was subdivided into multiple languages and datasets were provided for each one. The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification. I have participated in the task-C, that is, offense target identification. For preparing the proposed system, I have made use of Deep Learning networks like LSTMs and frameworks like Keras which combine the bag of words model with automatically generated sequence based features and manually extracted features from the given dataset. My system on training on 25% of the whole dataset achieves macro averaged f1 score of 47.763%. 1 Introduction The ever-growing amount of user-generated data on social media platforms be it Facebook, Twitter, blogs or any other electronic medium introduces new challenges in terms of automatic content moderation, especially regarding hate speech and offensive language detection. Not only is hate speech more likely to happen on the Internet, where anonymity is easily obtained and speakers are psychologically distant from their audience, but its online nature also gives it a far-reaching and determinative impact(Shaw, 2011). User content mostly consists of microposts, where the context of a post can be missing or inferred only from current events. Manual verification of each posting by a human moderator is infeasible due to the high amount of postings created every day. Consequently, automated detection of such attacking postings is the only feasible way to counter this kind of hostility. However, this task is challenging because natural language is fraught with ambiguities, and language in social media is extremely noisy. The classification system that would be prepared for the task, needed to be generalized for various test corpora as well. In this paper I have described the system consisting of a sequential pipeline with text feature extraction and classification as its main components. Firstly, a bag-of-words model is used for encoding the sentences into corresponding integer sequence. Thereafter, vectors are generated from these sequences and fed to a series of BiLSTM layers for training. Then a softmax layer is used for ternary classification into the corresponding offensive language categories. The rest of the paper has been organized as follows. Section 2 describes the data, on which, the task was performed. The methodology followed is described in Section 3. This is followed by the results and concluding remarks in Section 4 and 5 respectively. 2 Data The dataset that has been used to train and validate the model is an annotation following the tagset proposed in the Offensive Language Identification Dataset (OLID) (Zampieri et al., 2019a) and has been used in OffensEval 2019. This year more accurate and quantified dataset …

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