BJTU-Toshiba’s Submission to WMT22 Quality Estimation Shared Task
Hui Huang, Hui Di, Chunyou Li, Hanming Wu, Kazushige Ouchi, Yufeng Chen, Jian Liu, Jinan Xu
Abstract
This paper presents the BJTU-Toshiba joint submission for WMT 2022 quality estimation shared task. We only participate in Task 1 (quality prediction) of the shared task, focusing on the sentence-level MQM prediction. The techniques we experimented with include the integration of monolingual language models and the pre-finetuning of pre-trained representations. We tried two styles of pre-finetuning, namely Translation Language Modeling and Replaced Token Detection. We demonstrate the competitiveness of our system compared to the widely adopted XLM-RoBERTa baseline. Our system is also the top-ranking system on the Sentence-level MQM Prediction for the English-German language pairs.- Anthology ID:
- 2022.wmt-1.58
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- WMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 621–626
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.58
- DOI:
- Cite (ACL):
- Hui Huang, Hui Di, Chunyou Li, Hanming Wu, Kazushige Ouchi, Yufeng Chen, Jian Liu, and Jinan Xu. 2022. BJTU-Toshiba’s Submission to WMT22 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 621–626, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- BJTU-Toshiba’s Submission to WMT22 Quality Estimation Shared Task (Huang et al., WMT 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wmt-1.58.pdf