Abstract
In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.- Anthology ID:
- 2020.wmt-1.118
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1024–1028
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.118
- DOI:
- Cite (ACL):
- Dongjun Lee. 2020. Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation. In Proceedings of the Fifth Conference on Machine Translation, pages 1024–1028, Online. Association for Computational Linguistics.
- Cite (Informal):
- Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation (Lee, WMT 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.wmt-1.118.pdf