Web: http://arxiv.org/abs/2206.08053

June 17, 2022, 1:12 a.m. | Prantik Guha, Rudra Dhar, Dipankar Das

cs.CL updates on arXiv.org arxiv.org

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 …

arxiv code evaluation mixed text

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