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- W3103602928 abstract "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." @default.
- W3103602928 created "2020-11-23" @default.
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- W3103602928 date "2020-01-01" @default.
- W3103602928 modified "2023-10-09" @default.
- W3103602928 title "Abusive Language Detection using Syntactic Dependency Graphs" @default.
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- W3103602928 doi "https://doi.org/10.18653/v1/2020.alw-1.6" @default.
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