Don't Waste a Single Annotation: Improving Single-Label Classifiers
Through Soft Labels

Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels

9 November 2023

Carolina Scarton

Kalina Bontcheva

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Papers citing "Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels"

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Title
SCRum-9: Multilingual Stance Classification over Rumours on Social Media Yue Li Jake Vasilakes Zhixue Zhao Carolina Scarton 69 0 0 25 May 2025
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media Owen Cook Charlie Grimshaw Ben Wu Sophie Dillon Jack Hicks Luke Jones Thomas Smith Matyas Szert Xingyi Song 60 1 0 18 Oct 2024
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter Yida Mu Mali Jin Charles Grimshaw Carolina Scarton Kalina Bontcheva Xingyi Song 65 16 0 17 Jan 2023
Stop Measuring Calibration When Humans Disagree Joris Baan Wilker Aziz Barbara Plank Raquel Fernández 72 56 0 28 Oct 2022
Eliciting and Learning with Soft Labels from Every Annotator Katherine M. Collins Umang Bhatt Adrian Weller 71 46 0 02 Jul 2022
What Can We Learn from Collective Human Opinions on Natural Language Inference Data? Yixin Nie Xiang Zhou Joey Tianyi Zhou 86 138 0 07 Oct 2020
'Less Than One'-Shot Learning: Learning N Classes From M<N Samples Ilia Sucholutsky Matthias Schonlau VLM 109 43 0 17 Sep 2020
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter Martin Müller M. Salathé P. Kummervold VLM MedIm AI4MH 79 361 0 15 May 2020
Human uncertainty makes classification more robust Joshua C. Peterson Ruairidh M. Battleday Thomas Griffiths Olga Russakovsky OOD 64 304 0 19 Aug 2019
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 886 27,416 0 02 Dec 2015