# BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers

arXiv: https://arxiv.org/abs/2206.08680 · HTML: https://mlsanity.com/paper/2206.08680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [Unsupervised Multilingual Word Embeddings](https://mlsanity.com/paper/1808.08933/) (2018)
- [Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning](https://mlsanity.com/paper/1805.12070/) (2018)
- [MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation](https://mlsanity.com/paper/2107.11534/) (2021)
- [Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text](https://mlsanity.com/paper/2108.01861/) (2021)
- [Codeswitched Sentence Creation using Dependency Parsing](https://mlsanity.com/paper/2012.02990/) (2020)

## Abstract (arXiv)

Code-Mixed text data consists of sentences having words or phrases from more than one language. Most multi-lingual communities worldwide communicate using multiple languages, with English usually one of them. Hinglish is a Code-Mixed text composed of Hindi and English but written in Roman script. This paper aims to determine the factors influencing the quality of Code-Mixed text data generated by the system. For the HinglishEval task, the proposed model uses multi-lingual BERT to find the similarity between synthetically generated and human-generated sentences to predict the quality of synthetically generated Hinglish sentences.