2023
EMNLP
EMNLP 2023
Empowering a Metric with LLM-assisted Named Entity Annotation: HW-TSC’s Submission to the WMT23 Metrics Shared Task
Abstract
AbstractThis 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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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— translation evaluation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
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
,
Yanfei Jiang