Low-resource Sequence Labeling
Low-Resource Sequence Labeling is a sequence labeling paradigm distinguished by the scarcity of annotated training data, often characterized by limited label availability, minimal supervision, or constrained linguistic resources, necessitating specialized techniques to achieve effective model performance under such constraints.
19
Publications
758
Citations
66
Authors
24
Institutions
Publications per year
2017–2026
18
Parents
19
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling
Ying Lin, Shengqi Yang, Veselin Stoyanov et al. · 2018 · 118 citations · Full text
Low-Resource Name Tagging Learned with Weakly Labeled Data
Yixin Cao, Zikun Hu, Tat‐Seng Chua et al. · 2019 · 86 citations · Full text
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24
Google (United States)
Mountain View, United States
2 h-index
13 publications
519 citations

Saarland University
Saarbrücken, Germany
2 h-index
6 publications
102 citations
Shanghai Institute of Microsystem and Information Technology
Shanghai, China
1 h-index
2 publications
62 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
3 publications
195 citations
ShanghaiTech University
Shanghai, China
1 h-index
2 publications
62 citations

Technische Universität Darmstadt
Darmstadt, Germany
1 h-index
3 publications
42 citations
Alibaba Group (Cayman Islands)
George Town, Cayman Islands
1 h-index
4 publications
124 citations
Menlo School
Atherton, United States
1 h-index
1 publications
118 citations
1–8 of 24
Repository · 2 publications · 35 citations
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Conference · 2 publications · 27 citations
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TPAMI · 1 publications · 35 citations
1
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TACL · 1 publications · 33 citations
1
h-index
1 publications · 14 citations
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