%0 Conference Proceedings %T Abusive Language Detection using Syntactic Dependency Graphs %A Narang, Kanika %A Brew, Chris %Y Akiwowo, Seyi %Y Vidgen, Bertie %Y Prabhakaran, Vinodkumar %Y Waseem, Zeerak %S Proceedings of the Fourth Workshop on Online Abuse and Harms %D 2020 %8 November %I Association for Computational Linguistics %C Online %F narang-brew-2020-abusive %X Automated detection of abusive language online has become imperative. Current sequential models (LSTM) do not work well for long and complex sentences while bi-transformer models (BERT) are not computationally efficient for the task. We show that classifiers based on syntactic structure of the text, dependency graphical convolutional networks (DepGCNs) can achieve state-of-the-art performance on abusive language datasets. The overall performance is at par with of strong baselines such as fine-tuned BERT. Further, our GCN-based approach is much more efficient than BERT at inference time making it suitable for real-time detection. %R 10.18653/v1/2020.alw-1.6 %U https://aclanthology.org/2020.alw-1.6 %U https://doi.org/10.18653/v1/2020.alw-1.6 %P 44-53