2022
EMNLP
EMNLP 2022
Partial Could Be Better than Whole. HW-TSC 2022 Submission for the Metrics Shared Task
Abstract
AbstractIn this paper, we present the contribution of HW-TSC to WMT 2022 Metrics Shared Task. We propose one reference-based metric, HWTSC-EE-BERTScore*, and four referencefree metrics including HWTSC-Teacher-Sim, HWTSC-TLM, KG-BERTScore and CROSSQE. Among these metrics, HWTSC-Teacher-Sim and CROSS-QE are supervised, whereas HWTSC-EE-BERTScore*, HWTSC-TLM and KG-BERTScore are unsupervised. We use these metrics in the segment-level and systemlevel tracks. Overall, our systems achieve strong results for all language pairs on previous test sets and a new state-of-the-art in many sys-level case sets.
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Hot Topic Early Bird
— cross-lingual 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, Security & Privacy, Speech & Audio
Authors
Yilun Liu
,
Xiaosong Qiao
,
Zhanglin Wu
,
Su Chang
,
Min Zhang
,
Yanqing Zhao
,
Song Peng
,
Shimin Tao
,
Hao Yang
,
Ying Qin
,
Jiaxin Guo
,
Minghan Wang
,
Yinglu Li
,
Peng Li
,
Xiaofeng Zhao