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
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- 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
- Bibkey:
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6466.pdf
Export citation
@inproceedings{yankovskaya-etal-2018-quality,
title = "Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings",
author = {Yankovskaya, Elizaveta and
T{\"a}ttar, Andre and
Fishel, Mark},
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6466",
doi = "10.18653/v1/W18-6466",
pages = "816--821",
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.",
}
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%0 Conference Proceedings %T Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings %A Yankovskaya, Elizaveta %A Tättar, Andre %A Fishel, Mark %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Specia, Lucia %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Third Conference on Machine Translation: Shared Task Papers %D 2018 %8 October %I Association for Computational Linguistics %C Belgium, Brussels %F yankovskaya-etal-2018-quality %X 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. %R 10.18653/v1/W18-6466 %U https://aclanthology.org/W18-6466 %U https://doi.org/10.18653/v1/W18-6466 %P 816-821
Markdown (Informal)
[Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings](https://aclanthology.org/W18-6466) (Yankovskaya et al., WMT 2018)
- Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings (Yankovskaya et al., WMT 2018)
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.