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Crowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method
31 May 2023
Hasti Narimanzadeh
Arash Badie-Modiri
Iuliia Smirnova
T. H. Chen
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Papers citing
"Crowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method"
7 / 7 papers shown
Title
Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models
Lynnette Ng
Kokil Jaidka
Kaiyuan Tay
Hansin Ahuja
Niyati Chhaya
54
0
0
26 Mar 2025
(De)Noise: Moderating the Inconsistency Between Human Decision-Makers
Nina Grgić-Hlavca
Junaid Ali
Krishna P. Gummadi
Jennifer Wortman Vaughan
36
1
0
15 Jul 2024
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
Timon Ziegenbein
Gabriella Skitalinskaya
Alireza Bayat Makou
Henning Wachsmuth
LLMAG
KELM
29
5
0
05 Jun 2024
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
Eve Fleisig
Su Lin Blodgett
Dan Klein
Zeerak Talat
29
13
0
09 May 2024
ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?
Fan Huang
Haewoon Kwak
Kunwoo Park
Jisun An
ALM
ELM
AI4MH
37
12
0
26 Mar 2024
Toward Effective Automated Content Analysis via Crowdsourcing
Jiele Wu
Chau-Wai Wong
Xinyan Zhao
Xianpeng Liu
8
4
0
12 Jan 2021
Capturing Ambiguity in Crowdsourcing Frame Disambiguation
Anca Dumitrache
Lora Aroyo
Chris Welty
FedML
30
31
0
01 May 2018
1