Abstract
This paper describes the system description for the HinglishEval challenge at INLG 2022. The goal of this task was to investigate the factors influencing the quality of the code- mixed text generation system. The task was divided into two subtasks, quality rating prediction and annotators’ disagreement prediction of the synthetic Hinglish dataset. We attempted to solve these tasks using sentence-level embeddings, which are obtained from mean pooling the contextualized word embeddings for all input tokens in our text. We experimented with various classifiers on top of the embeddings produced for respective tasks. Our best-performing system ranked 1st on subtask B and 3rd on subtask A. We make our code available here: https://github.com/nikhilbyte/Hinglish-qEval- Anthology ID:
- 2022.inlg-genchal.5
- Volume:
- Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges
- Month:
- July
- Year:
- 2022
- Address:
- Waterville, Maine, USA and virtual meeting
- Editors:
- Samira Shaikh, Thiago Ferreira, Amanda Stent
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–34
- Language:
- URL:
- https://aclanthology.org/2022.inlg-genchal.5
- DOI:
- Cite (ACL):
- Nikhil Singh. 2022. niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges, pages 31–34, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text (Singh, INLG 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.inlg-genchal.5.pdf