@InProceedings{husseiniorabi-EtAl:2018:W18-44,
  author    = {Husseini Orabi, Ahmed  and  Husseini Orabi, Mahmoud  and  Huang, Qianjia  and  Inkpen, Diana  and  Van Bruwaene, David},
  title     = {Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text},
  booktitle = {Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {159--165},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/W18-4419}
}