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Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text

In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detection from text. Our approach provides a robust shared feature representation for multi-task learning by detecting contrasts and similarities among polarity and neutral classes. We participated in the cyber-aggression shared task under the team name uOttawa. We report 59.74{\%} F1 performance for the Facebook test set and 56.9{\%} for the Twitter test set, for detecting aggression from text.

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