Abstract
We obtain new results using referential translation machines (RTMs) with predictions mixed and stacked to obtain a better mixture of experts prediction. We are able to achieve better results than the baseline model in Task 1 subtasks. Our stacking results significantly improve the results on the training sets but decrease the test set results. RTMs can achieve to become the 5th among 13 models in ru-en subtask and 5th in the multilingual track of sentence-level Task 1 based on MAE.- Anthology ID:
- 2020.wmt-1.114
- 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:
- 999–1003
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.114
- DOI:
- Cite (ACL):
- Ergun Biçici. 2020. RTM Ensemble Learning Results at Quality Estimation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 999–1003, Online. Association for Computational Linguistics.
- Cite (Informal):
- RTM Ensemble Learning Results at Quality Estimation Task (Biçici, WMT 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.wmt-1.114.pdf