An Attention Ensemble Based Approach for Multilabel Profanity Detection The amount of usergenerated content in the cyberspace keeps increasing in the 21st century. However, it has also meant an increase in the number of cyber abuse and bullying incidents being reported. Use of profane text by individuals threatens the liberty and integrity of the digital space. Manual moderation and reporting mechanisms have been traditionally used to keep a check on such profane text. Dependency on human interpretation and delay in results have been the biggest obstacles in this system. Previous deep learningbased approaches to automate the process have involved use of traditional convolution and recurrence based sequential models. However, these models tend to be computationally expensive and have higher memory requirement. Further, they tend to produce state of the art results in binary classification but perform relatively poorly on multilabel tasks, owing to less flexibility in architecture. In todayu0027s world, classifying text in a binary way is no longer sufficient and thus a flexible solution able to generalize well on multilabel text is the need of the hour. In this paper, we propose a multihead attentionbased approach for detection of profane text. We couple our model with power weighted average ensembling techniques to further improve the performance. The proposed approach does not have additional memory requirement and is less complex as compared to previous approaches. The improved results obtained by our model on publicly available realworld data further validate the same. Flexible, lightweight models which can handle multilabel text well can prove to be crucial in cracking down on social evils in the digital space.

A Unified System for Aggression Identification in English CodeMixed and UniLingual Texts. Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. Majority of such conversations contains codemixed languages28. Additionally, the way used to express thought or communication style also changes from one social media platform to another platform (e.g., communication styles are different in twitter and Facebook). These all have increased the complexity of the problem. To solve these problems, we have introduced a unified and robust multimodal deep learning architecture which works for English codemixed dataset and unilingual English dataset both.The devised system, uses psycholinguistic features and very basic linguistic features. Our multimodal deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and Disconnected RNN(with Glove and FastText embedding, both). Finally, the system takes the decision based on model averaging. We evaluated our system on English CodeMixed TRAC 2018 dataset and unilingual English dataset obtained from Kaggle. Experimental results show that our proposed system outperforms all the previous approaches on English codemixed dataset and unilingual English dataset.