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

Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen, David Van Bruwaene


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.
Anthology ID:
W18-4419
Volume:
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venues:
COLING | TRAC | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
159–165
Language:
URL:
https://aclanthology.org/W18-4419
DOI:
Bibkey:
Cite (ACL):
Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen, and David Van Bruwaene. 2018. Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 159–165, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text (Husseini Orabi et al., 2018)
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PDF:
https://aclanthology.org/W18-4419.pdf