Abstract
In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.- Anthology ID:
- 2020.wmt-1.118
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1024–1028
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.118
- DOI:
- Bibkey:
- Cite (ACL):
- Dongjun Lee. 2020. Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation. In Proceedings of the Fifth Conference on Machine Translation, pages 1024–1028, Online. Association for Computational Linguistics.
- Cite (Informal):
- Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation (Lee, WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.118.pdf
- Video:
- https://slideslive.com/38939546
Export citation
@inproceedings{lee-2020-two,
title = "Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation",
author = "Lee, Dongjun",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.118",
pages = "1024--1028",
abstract = "In this paper, we describe the Bering Lab{'}s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.",
}
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%0 Conference Proceedings %T Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation %A Lee, Dongjun %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F lee-2020-two %X In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions. %U https://aclanthology.org/2020.wmt-1.118 %P 1024-1028
Markdown (Informal)
[Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation](https://aclanthology.org/2020.wmt-1.118) (Lee, WMT 2020)
- Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation (Lee, WMT 2020)
ACL
- Dongjun Lee. 2020. Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation. In Proceedings of the Fifth Conference on Machine Translation, pages 1024–1028, Online. Association for Computational Linguistics.