@inproceedings{chen-etal-2021-hw,
title = "{HW}-{TSC}{'}s Participation at {WMT} 2021 Quality Estimation Shared Task",
author = "Chen, Yimeng and
Su, Chang and
Zhang, Yingtao and
Wang, Yuxia and
Geng, Xiang and
Yang, Hao and
Tao, Shimin and
Jiaxin, Guo and
Minghan, Wang and
Zhang, Min and
Liu, Yujia and
Huang, Shujian",
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-1.92",
pages = "890--896",
abstract = "This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task. We participated in all of the three sub-tasks, including Sentence-Level Direct Assessment (DA) task, Word and Sentence-Level Post-editing Effort task and Critical Error Detection task, in all language pairs. Our systems employ the framework of Predictor-Estimator, concretely with a pre-trained XLM-Roberta as Predictor and task-specific classifier or regressor as Estimator. For all tasks, we improve our systems by incorporating post-edit sentence or additional high-quality translation sentence in the way of multitask learning or encoding it with predictors directly. Moreover, in zero-shot setting, our data augmentation strategy based on Monte-Carlo Dropout brings up significant improvement on DA sub-task. Notably, our submissions achieve remarkable results over all tasks.",
}
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<abstract>This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task. We participated in all of the three sub-tasks, including Sentence-Level Direct Assessment (DA) task, Word and Sentence-Level Post-editing Effort task and Critical Error Detection task, in all language pairs. Our systems employ the framework of Predictor-Estimator, concretely with a pre-trained XLM-Roberta as Predictor and task-specific classifier or regressor as Estimator. For all tasks, we improve our systems by incorporating post-edit sentence or additional high-quality translation sentence in the way of multitask learning or encoding it with predictors directly. Moreover, in zero-shot setting, our data augmentation strategy based on Monte-Carlo Dropout brings up significant improvement on DA sub-task. Notably, our submissions achieve remarkable results over all tasks.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
%A Chen, Yimeng
%A Su, Chang
%A Zhang, Yingtao
%A Wang, Yuxia
%A Geng, Xiang
%A Yang, Hao
%A Tao, Shimin
%A Jiaxin, Guo
%A Minghan, Wang
%A Zhang, Min
%A Liu, Yujia
%A Huang, Shujian
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-hw
%X This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task. We participated in all of the three sub-tasks, including Sentence-Level Direct Assessment (DA) task, Word and Sentence-Level Post-editing Effort task and Critical Error Detection task, in all language pairs. Our systems employ the framework of Predictor-Estimator, concretely with a pre-trained XLM-Roberta as Predictor and task-specific classifier or regressor as Estimator. For all tasks, we improve our systems by incorporating post-edit sentence or additional high-quality translation sentence in the way of multitask learning or encoding it with predictors directly. Moreover, in zero-shot setting, our data augmentation strategy based on Monte-Carlo Dropout brings up significant improvement on DA sub-task. Notably, our submissions achieve remarkable results over all tasks.
%U https://aclanthology.org/2021.wmt-1.92
%P 890-896
Markdown (Informal)
[HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task](https://aclanthology.org/2021.wmt-1.92) (Chen et al., WMT 2021)
ACL
- Yimeng Chen, Chang Su, Yingtao Zhang, Yuxia Wang, Xiang Geng, Hao Yang, Shimin Tao, Guo Jiaxin, Wang Minghan, Min Zhang, Yujia Liu, and Shujian Huang. 2021. HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 890–896, Online. Association for Computational Linguistics.