# PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality

arXiv: https://arxiv.org/abs/2206.07988 · HTML: https://mlsanity.com/paper/2206.07988/

## 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 16 Jun 2022.

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

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [GLUECoS : An Evaluation Benchmark for Code-Switched NLP](https://mlsanity.com/paper/2004.12376/) (2020)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text](https://mlsanity.com/paper/2108.01861/) (2021)

## Abstract (arXiv)

Code-Mixing is a phenomenon of mixing two or more languages in a speech event and is prevalent in multilingual societies. Given the low-resource nature of Code-Mixing, machine generation of code-mixed text is a prevalent approach for data augmentation. However, evaluating the quality of such machine generated code-mixed text is an open problem. In our submission to HinglishEval, a shared-task collocated with INLG2022, we attempt to build models factors that impact the quality of synthetically generated code-mix text by predicting ratings for code-mix quality.