Abstract
In this paper we describe a system submit- ted 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 Av- erage rating score and Disagreement score of the synthetic Hinglish dataset. In our mod- els, 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.- Anthology ID:
- 2022.inlg-genchal.7
- Volume:
- Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges
- Month:
- July
- Year:
- 2022
- Address:
- Waterville, Maine, USA and virtual meeting
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 39–42
- Language:
- URL:
- https://aclanthology.org/2022.inlg-genchal.7
- DOI:
- Cite (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.
- Cite (Informal):
- JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text (Guha et al., INLG 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.inlg-genchal.7.pdf