  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 code-mixed languages[28]. Additionally, the way
used to express thought or communication style also changes from one social
media plat-form 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
multi-modal deep learning architecture which works for English code-mixed
dataset and uni-lingual English dataset both.The devised system, uses
psycho-linguistic features and very ba-sic linguistic features. Our multi-modal
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
Code-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained from
Kaggle. Experimental results show that our proposed system outperforms all the
previous approaches on English code-mixed dataset and uni-lingual English
dataset.
