Abstract
We obtain new results using referential translation machines with increased number of learning models in the set of results that are stacked to obtain a better mixture of experts prediction. We combine features extracted from the word-level predictions with the sentence- or document-level features, which significantly improve the results on the training sets but decrease the test set results.- Anthology ID:
- W19-5405
- 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:
- 73–77
- Language:
- URL:
- https://aclanthology.org/W19-5405
- DOI:
- 10.18653/v1/W19-5405
- Cite (ACL):
- Ergun Biçici. 2019. RTM Stacking Results for Machine Translation Performance Prediction. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 73–77, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- RTM Stacking Results for Machine Translation Performance Prediction (Biçici, WMT 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W19-5405.pdf