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The last few years have witnessed an exponential rise in the propagation ofoffensive text on social media. Identification of this text with high precisionis crucial for the well-being of society. Most of the existing approaches tendto give high toxicity scores to innocuous statements (e.g., "I am a gay man").These false positives result from over-generalization on the training datawhere specific terms in the statement may have been used in a pejorative sense(e.g., "gay"). Emphasis on such words alone can lead to discrimination againstthe classes these systems are designed to protect. In this paper, we addressthe problem of offensive language detection on Twitter, while also detectingthe type and the target of the offence. We propose a novel approach calledSyLSTM, which integrates syntactic features in the form of the dependency parsetree of a sentence and semantic features in the form of word embeddings into adeep learning architecture using a Graph Convolutional Network. Results showthat the proposed approach significantly outperforms the state-of-the-art BERTmodel with orders of magnitude fewer number of parameters.

Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks