UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks
Felipe Sánchez-Martínez, Miquel Esplà-Gomis, Mikel L. Forcada
Abstract
We describe the Universitat d’Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018. Our approach to word-level MT QE builds on previous work to mark the words in the machine-translated sentence as OK or BAD, and is extended to determine if a word or sequence of words need to be inserted in the gap after each word. Our sentence-level submission simply uses the edit operations predicted by the word-level approach to approximate TER. The method presented ranked first in the sub-task of identifying insertions in gaps for three out of the six datasets, and second in the rest of them.- Anthology ID:
- W18-6464
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 801–808
- Language:
- URL:
- https://aclanthology.org/W18-6464
- DOI:
- 10.18653/v1/W18-6464
- Cite (ACL):
- Felipe Sánchez-Martínez, Miquel Esplà-Gomis, and Mikel L. Forcada. 2018. UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 801–808, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks (Sánchez-Martínez et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-6464.pdf