  With a sharp rise in fluency and users of "Hinglish" in linguistically
diverse country, India, it has increasingly become important to analyze social
content written in this language in platforms such as Twitter, Reddit,
Facebook. This project focuses on using deep learning techniques to tackle a
classification problem in categorizing social content written in Hindi-English
into Abusive, Hate-Inducing and Not offensive categories. We utilize
bi-directional sequence models with easy text augmentation techniques such as
synonym replacement, random insertion, random swap, and random deletion to
produce a state of the art classifier that outperforms the previous work done
on analyzing this dataset.
