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
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- 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/nschneid-patch-2/W19-5405.pdf