# JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text

arXiv: https://arxiv.org/abs/2206.08053 · HTML: https://mlsanity.com/paper/2206.08053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 16 Jun 2022.

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

- [PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation](https://mlsanity.com/paper/2004.09447/) (2020)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017](https://mlsanity.com/paper/1803.06745/) (2018)
- [Predicting the Semantic Textual Similarity with Siamese CNN and LSTM](https://mlsanity.com/paper/1810.10641/) (2018)
- [Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text](https://mlsanity.com/paper/2108.01861/) (2021)
- [Language Model Augmented Relevance Score](https://mlsanity.com/paper/2108.08485/) (2021)
- [Unsupervised Sentiment Analysis for Code-mixed Data](https://mlsanity.com/paper/2001.11384/) (2020)

## Abstract (arXiv)

In this paper we describe a system submitted to the INLG 2022 Generation Challenge (GenChal) on Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text. We implement a Bi-LSTM-based neural network model to predict the Average rating score and Disagreement score of the synthetic Hinglish dataset. In our models, we used word embeddings for English and Hindi data, and one hot encodings for Hinglish data. We achieved a F1 score of 0.11, and mean squared error of 6.0 in the average rating score prediction task. In the task of Disagreement score prediction, we achieve a F1 score of 0.18, and mean squared error of 5.0.