deepQuest-py: Large and Distilled Models for Quality Estimation
Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia
Abstract
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions. deepQuest-py is available at https://github.com/sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.- Anthology ID:
- 2021.emnlp-demo.42
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Heike Adel, Shuming Shi
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 382–389
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2021.emnlp-demo.42/
- DOI:
- 10.18653/v1/2021.emnlp-demo.42
- Cite (ACL):
- Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, and Lucia Specia. 2021. deepQuest-py: Large and Distilled Models for Quality Estimation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 382–389, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- deepQuest-py: Large and Distilled Models for Quality Estimation (Alva-Manchego et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2021.emnlp-demo.42.pdf
- Code
- sheffieldnlp/deepQuest-py
- Data
- MLQE