"Hinglish" Language -- Modeling a Messy Code-Mixed Language
This work addresses the need to analyze offensive content in Hinglish for social media platforms in India, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of classifying social media content in Hinglish (Hindi-English code-mixed language) into abusive, hate-inducing, and not offensive categories, achieving state-of-the-art performance by using bidirectional sequence models with text augmentation techniques.
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.