Abstract
Quality estimation (QE) of machine translation (MT) systems is a task of growing importance. It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions. In this work, we describe our submission system in WMT 2019 sentence-level QE task. We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy. The strategy is similar to ELMo, but additionally conditions on source sentences. Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE. In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.- Anthology ID:
- W19-5411
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 106–111
- Language:
- URL:
- https://aclanthology.org/W19-5411
- DOI:
- 10.18653/v1/W19-5411
- Cite (ACL):
- Junpei Zhou, Zhisong Zhang, and Zecong Hu. 2019. SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 106–111, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation (Zhou et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W19-5411.pdf