Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text
Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Zaiu Inkpen, David Van Bruwaene · 2018
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.