  The last few years have witnessed an exponential rise in the propagation of
offensive text on social media. Identification of this text with high precision
is crucial for the well-being of society. Most of the existing approaches tend
to give high toxicity scores to innocuous statements (e.g., "I am a gay man").
These false positives result from over-generalization on the training data
where 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 against
the classes these systems are designed to protect. In this paper, we address
the problem of offensive language detection on Twitter, while also detecting
the type and the target of the offence. We propose a novel approach called
SyLSTM, which integrates syntactic features in the form of the dependency parse
tree of a sentence and semantic features in the form of word embeddings into a
deep learning architecture using a Graph Convolutional Network. Results show
that the proposed approach significantly outperforms the state-of-the-art BERT
model with orders of magnitude fewer number of parameters.
