Knowledge Distillation For Mt
Knowledge Distillation for MT is a model compression technique in machine translation that transfers the learned linguistic and contextual knowledge from a large, high-performing teacher model to a smaller student model by mimicking its soft-label output distributions, thereby enabling efficient inference while preserving translation quality.
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2017–2026
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Sequence-Level Knowledge Distillation
Yoon Kim, Alexander M. Rush · 2016 · 775 citations · Full text
Non-Autoregressive Neural Machine Translation
Jiatao Gu, James Bradbury, Caiming Xiong et al. · arXiv (Cornell University) · 2017 · 449 citations · Full text
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
Peter West, Chandra Bhagavatula, Jack Hessel et al. · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022 · 150 citations · Full text
End-to-End Speech Translation with Knowledge Distillation
Yuchen Liu, Hao Xiong, Jiajun Zhang et al. · 2019 · 144 citations
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Tencent (China)
Shenzhen, China
5 h-index
11 publications
340 citations
University of Chinese Academy of Sciences
Beijing, China
5 h-index
12 publications
612 citations
Microsoft (United States)
Redmond, United States
3 h-index
11 publications
662 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
9 publications
531 citations
Huawei Technologies (Sweden)
Stockholm, Sweden
3 h-index
10 publications
515 citations
Baidu (China)
Beijing, China
3 h-index
11 publications
805 citations
Institute of Computing Technology
Beijing, China
3 h-index
8 publications
161 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
3 h-index
8 publications
818 citations
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Repository · 16 publications · 1.3K citations
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Conference · 5 publications · 234 citations
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2 publications · 56 citations
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2 publications · 37 citations
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2 publications · 55 citations
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