@inproceedings{yu-etal-2023-hw,
title = "{HW}-{TSC}{'}s Participation in the {WMT} 2023 Automatic Post Editing Shared Task",
author = "Yu, Jiawei and
Zhang, Min and
Yanqing, Zhao and
Zhao, Xiaofeng and
Li, Yuang and
Chang, Su and
Li, Yinglu and
Miaomiao, Ma and
Tao, Shimin and
Yang, Hao",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.85",
doi = "10.18653/v1/2023.wmt-1.85",
pages = "926--930",
abstract = "The paper presents the submission by HW-TSC in the WMT 2023 Automatic Post Editing (APE) shared task for the English-Marathi (En-Mr) language pair. Our method encompasses several key steps. First, we pre-train an APE model by utilizing synthetic APE data provided by the official task organizers. Then, we fine-tune the model by employing real APE data. For data augmentation, we incorporate candidate translations obtained from an external Machine Translation (MT) system. Furthermore, we integrate the En-Mr parallel corpus from the Flores-200 dataset into our training data. To address the overfitting issue, we employ R-Drop during the training phase. Given that APE systems tend to exhibit a tendency of {`}over-correction{'}, we employ a sentence-level Quality Estimation (QE) system to select the final output, deciding between the original translation and the corresponding output generated by the APE model. Our experiments demonstrate that pre-trained APE models are effective when being fine-tuned with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our approach improves the TER and BLEU scores on the development set by -2.42 and +3.76 points, respectively.",
}
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<abstract>The paper presents the submission by HW-TSC in the WMT 2023 Automatic Post Editing (APE) shared task for the English-Marathi (En-Mr) language pair. Our method encompasses several key steps. First, we pre-train an APE model by utilizing synthetic APE data provided by the official task organizers. Then, we fine-tune the model by employing real APE data. For data augmentation, we incorporate candidate translations obtained from an external Machine Translation (MT) system. Furthermore, we integrate the En-Mr parallel corpus from the Flores-200 dataset into our training data. To address the overfitting issue, we employ R-Drop during the training phase. Given that APE systems tend to exhibit a tendency of ‘over-correction’, we employ a sentence-level Quality Estimation (QE) system to select the final output, deciding between the original translation and the corresponding output generated by the APE model. Our experiments demonstrate that pre-trained APE models are effective when being fine-tuned with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our approach improves the TER and BLEU scores on the development set by -2.42 and +3.76 points, respectively.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Participation in the WMT 2023 Automatic Post Editing Shared Task
%A Yu, Jiawei
%A Zhang, Min
%A Yanqing, Zhao
%A Zhao, Xiaofeng
%A Li, Yuang
%A Chang, Su
%A Li, Yinglu
%A Miaomiao, Ma
%A Tao, Shimin
%A Yang, Hao
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yu-etal-2023-hw
%X The paper presents the submission by HW-TSC in the WMT 2023 Automatic Post Editing (APE) shared task for the English-Marathi (En-Mr) language pair. Our method encompasses several key steps. First, we pre-train an APE model by utilizing synthetic APE data provided by the official task organizers. Then, we fine-tune the model by employing real APE data. For data augmentation, we incorporate candidate translations obtained from an external Machine Translation (MT) system. Furthermore, we integrate the En-Mr parallel corpus from the Flores-200 dataset into our training data. To address the overfitting issue, we employ R-Drop during the training phase. Given that APE systems tend to exhibit a tendency of ‘over-correction’, we employ a sentence-level Quality Estimation (QE) system to select the final output, deciding between the original translation and the corresponding output generated by the APE model. Our experiments demonstrate that pre-trained APE models are effective when being fine-tuned with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our approach improves the TER and BLEU scores on the development set by -2.42 and +3.76 points, respectively.
%R 10.18653/v1/2023.wmt-1.85
%U https://aclanthology.org/2023.wmt-1.85
%U https://doi.org/10.18653/v1/2023.wmt-1.85
%P 926-930
Markdown (Informal)
[HW-TSC’s Participation in the WMT 2023 Automatic Post Editing Shared Task](https://aclanthology.org/2023.wmt-1.85) (Yu et al., WMT 2023)
ACL
- Jiawei Yu, Min Zhang, Zhao Yanqing, Xiaofeng Zhao, Yuang Li, Su Chang, Yinglu Li, Ma Miaomiao, Shimin Tao, and Hao Yang. 2023. HW-TSC’s Participation in the WMT 2023 Automatic Post Editing Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 926–930, Singapore. Association for Computational Linguistics.