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        <title>JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text
%A Guha, Prantik
%A Dhar, Rudra
%A Das, Dipankar
%Y Shaikh, Samira
%Y Ferreira, Thiago
%Y Stent, Amanda
%S Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges
%D 2022
%8 July
%I Association for Computational Linguistics
%C Waterville, Maine, USA and virtual meeting
%F guha-etal-2022-ju
%X 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.
%U https://aclanthology.org/2022.inlg-genchal.7
%P 39-42

Markdown (Informal)

[JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text](https://aclanthology.org/2022.inlg-genchal.7) (Guha et al., INLG 2022)

• JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text (Guha et al., INLG 2022)

ACL

• Prantik Guha, Rudra Dhar, and Dipankar Das. 2022. JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges, pages 39–42, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.