@inproceedings{wan-etal-2021-robleurt,
title = "{R}o{BLEURT} Submission for {WMT}2021 Metrics Task",
author = "Wan, Yu and
Liu, Dayiheng and
Yang, Baosong and
Bi, Tianchi and
Zhang, Haibo and
Chen, Boxing and
Luo, Weihua and
Wong, Derek F. and
Chao, Lidia S.",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.114",
pages = "1053--1058",
abstract = "In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy. Experimental results show that our model reaching state-of-the-art correlations with the WMT2020 human annotations upon 8 out of 10 to-English language pairs.",
}
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<abstract>In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy. Experimental results show that our model reaching state-of-the-art correlations with the WMT2020 human annotations upon 8 out of 10 to-English language pairs.</abstract>
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%0 Conference Proceedings
%T RoBLEURT Submission for WMT2021 Metrics Task
%A Wan, Yu
%A Liu, Dayiheng
%A Yang, Baosong
%A Bi, Tianchi
%A Zhang, Haibo
%A Chen, Boxing
%A Luo, Weihua
%A Wong, Derek F.
%A Chao, Lidia S.
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F wan-etal-2021-robleurt
%X In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy. Experimental results show that our model reaching state-of-the-art correlations with the WMT2020 human annotations upon 8 out of 10 to-English language pairs.
%U https://aclanthology.org/2021.wmt-1.114
%P 1053-1058
Markdown (Informal)
[RoBLEURT Submission for WMT2021 Metrics Task](https://aclanthology.org/2021.wmt-1.114) (Wan et al., WMT 2021)
ACL
- Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, and Lidia S. Chao. 2021. RoBLEURT Submission for WMT2021 Metrics Task. In Proceedings of the Sixth Conference on Machine Translation, pages 1053–1058, Online. Association for Computational Linguistics.