# "Hinglish" Language -- Modeling a Messy Code-Mixed Language

arXiv: https://arxiv.org/abs/1912.13109 · HTML: https://mlsanity.com/paper/1912.13109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [AtteSTNet -- An attention and subword tokenization based approach for code-switched text hate speech detection](https://mlsanity.com/paper/2112.11479/) (2021)

## Abstract (arXiv)

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.