@inproceedings{guha-etal-2022-ju,
    title = "{JU}{\_}{NLP} at {H}inglish{E}val: Quality Evaluation of the Low-Resource Code-Mixed {H}inglish Text",
    author = "Guha, Prantik  and
      Dhar, Rudra  and
      Das, Dipankar",
    editor = "Shaikh, Samira  and
      Ferreira, Thiago  and
      Stent, Amanda",
    booktitle = "Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges",
    month = jul,
    year = "2022",
    address = "Waterville, Maine, USA and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.inlg-genchal.7/",
    pages = "39--42",
    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."
}