Abstract
This paper describes the submissions of the team from the University of Tartu for the sentence-level Quality Estimation shared task of WMT18. The proposed models use features based on attention weights of a neural machine translation system and cross-lingual phrase embeddings as input features of a regression model. Two of the proposed models require only a neural machine translation system with an attention mechanism with no additional resources. Results show that combining neural networks and baseline features leads to significant improvements over the baseline features alone.- Anthology ID:
- W18-6466
- 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:
- 816–821
- Language:
- URL:
- https://aclanthology.org/W18-6466
- DOI:
- 10.18653/v1/W18-6466
- Cite (ACL):
- Elizaveta Yankovskaya, Andre Tättar, and Mark Fishel. 2018. Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 816–821, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings (Yankovskaya et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-6466.pdf