@inproceedings{wu-etal-2023-empowering,
title = "Empowering a Metric with {LLM}-assisted Named Entity Annotation: {HW}-{TSC}{'}s Submission to the {WMT}23 Metrics Shared Task",
author = "Wu, Zhanglin and
Liu, Yilun and
Zhang, Min and
Zhao, Xiaofeng and
Zhu, Junhao and
Zhu, Ming and
Qiao, Xiaosong and
Zhang, Jingfei and
Miaomiao, Ma and
Yanqing, Zhao and
Peng, Song and
Tao, Shimin and
Yang, Hao and
Jiang, Yanfei",
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-1.70",
doi = "10.18653/v1/2023.wmt-1.70",
pages = "822--828",
abstract = "This paper presents the submission of Huawei Translation Service Center (HW-TSC) to the WMT23 metrics shared task, in which we submit two metrics: KG-BERTScore and HWTSC-EE-Metric. Among them, KG-BERTScore is our primary submission for the reference-free metric, which can provide both segment-level and system-level scoring. While HWTSC-EE-Metric is our primary submission for the reference-based metric, which can only provide system-level scoring. Overall, our metrics show relatively high correlations with MQM scores on the metrics tasks of previous years. Especially on system-level scoring tasks, our metrics achieve new state-of-the-art in many language pairs.",
}
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<abstract>This paper presents the submission of Huawei Translation Service Center (HW-TSC) to the WMT23 metrics shared task, in which we submit two metrics: KG-BERTScore and HWTSC-EE-Metric. Among them, KG-BERTScore is our primary submission for the reference-free metric, which can provide both segment-level and system-level scoring. While HWTSC-EE-Metric is our primary submission for the reference-based metric, which can only provide system-level scoring. Overall, our metrics show relatively high correlations with MQM scores on the metrics tasks of previous years. Especially on system-level scoring tasks, our metrics achieve new state-of-the-art in many language pairs.</abstract>
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%0 Conference Proceedings
%T Empowering a Metric with LLM-assisted Named Entity Annotation: HW-TSC’s Submission to the WMT23 Metrics Shared Task
%A Wu, Zhanglin
%A Liu, Yilun
%A Zhang, Min
%A Zhao, Xiaofeng
%A Zhu, Junhao
%A Zhu, Ming
%A Qiao, Xiaosong
%A Zhang, Jingfei
%A Miaomiao, Ma
%A Yanqing, Zhao
%A Peng, Song
%A Tao, Shimin
%A Yang, Hao
%A Jiang, Yanfei
%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 wu-etal-2023-empowering
%X This paper presents the submission of Huawei Translation Service Center (HW-TSC) to the WMT23 metrics shared task, in which we submit two metrics: KG-BERTScore and HWTSC-EE-Metric. Among them, KG-BERTScore is our primary submission for the reference-free metric, which can provide both segment-level and system-level scoring. While HWTSC-EE-Metric is our primary submission for the reference-based metric, which can only provide system-level scoring. Overall, our metrics show relatively high correlations with MQM scores on the metrics tasks of previous years. Especially on system-level scoring tasks, our metrics achieve new state-of-the-art in many language pairs.
%R 10.18653/v1/2023.wmt-1.70
%U https://aclanthology.org/2023.wmt-1.70
%U https://doi.org/10.18653/v1/2023.wmt-1.70
%P 822-828
Markdown (Informal)
[Empowering a Metric with LLM-assisted Named Entity Annotation: HW-TSC’s Submission to the WMT23 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.70) (Wu et al., WMT 2023)
ACL
- Zhanglin Wu, Yilun Liu, Min Zhang, Xiaofeng Zhao, Junhao Zhu, Ming Zhu, Xiaosong Qiao, Jingfei Zhang, Ma Miaomiao, Zhao Yanqing, Song Peng, Shimin Tao, Hao Yang, and Yanfei Jiang. 2023. Empowering a Metric with LLM-assisted Named Entity Annotation: HW-TSC’s Submission to the WMT23 Metrics Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 822–828, Singapore. Association for Computational Linguistics.