Kanika Narang


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Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)
Kanika Narang | Aida Mostafazadeh Davani | Lambert Mathias | Bertie Vidgen | Zeerak Talat
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)


Abusive Language Detection using Syntactic Dependency Graphs
Kanika Narang | Chris Brew
Proceedings of the Fourth Workshop on Online Abuse and Harms

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.


An Empirical Assessment of Contemporary Online Media in Ad-Hoc Corpus Creation for Social Events
Kanika Narang | Seema Nagar | Sameep Mehta | L V Subramaniam | Kuntal Dey
Proceedings of the Sixth International Joint Conference on Natural Language Processing