NJU Submissions for the WMT19 Quality Estimation Shared Task

Hou Qi


Abstract
In this paper, we describe the submissions of the team from Nanjing University for the WMT19 sentence-level Quality Estimation (QE) shared task on English-German language pair. We develop two approaches based on a two-stage neural QE model consisting of a feature extractor and a quality estimator. More specifically, one of the proposed approaches employs the translation knowledge between the two languages from two different translation directions; while the other one employs extra monolingual knowledge from both source and target sides, obtained by pre-training deep self-attention networks. To efficiently train these two-stage models, a joint learning training method is applied. Experiments show that the ensemble model of the above two models achieves the best results on the benchmark dataset of the WMT17 sentence-level QE shared task and obtains competitive results in WMT19, ranking 3rd out of 10 submissions.
Anthology ID:
W19-5409
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:
95–100
Language:
URL:
https://aclanthology.org/W19-5409
DOI:
10.18653/v1/W19-5409
Bibkey:
Cite (ACL):
Hou Qi. 2019. NJU Submissions for the WMT19 Quality Estimation Shared Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 95–100, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
NJU Submissions for the WMT19 Quality Estimation Shared Task (Qi, WMT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5409.pdf