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In this paper we describe a system submitted to the INLG 2022 GenerationChallenge (GenChal) on Quality Evaluation of the Low-Resource SyntheticallyGenerated Code-Mixed Hinglish Text. We implement a Bi-LSTM-based neural networkmodel to predict the Average rating score and Disagreement score of thesynthetic Hinglish dataset. In our models, we used word embeddings for Englishand Hindi data, and one hot encodings for Hinglish data. We achieved a F1 scoreof 0.11, and mean squared error of 6.0 in the average rating score predictiontask. In the task of Disagreement score prediction, we achieve a F1 score of0.18, and mean squared error of 5.0.

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