Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings

Elizaveta Yankovskaya, Andre Tättar, Mark Fishel

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://preview.aclanthology.org/ingest-acl-2023-videos/W18-6466.pdf
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