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.
@article{arxiv.2206.08053,
title = {JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text},
author = {Prantik Guha and Rudra Dhar and Dipankar Das},
journal= {arXiv preprint arXiv:2206.08053},
year = {2022}
}