@inproceedings{homan-etal-2022-annotator,
title = "Annotator Response Distributions as a Sampling Frame",
author = "Homan, Christopher and
Weerasooriya, Tharindu Cyril and
Aroyo, Lora and
Welty, Chris",
editor = "Abercrombie, Gavin and
Basile, Valerio and
Tonelli, Sara and
Rieser, Verena and
Uma, Alexandra",
booktitle = "Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.nlperspectives-1.8",
pages = "56--65",
abstract = "Annotator disagreement is often dismissed as noise or the result of poor annotation process quality. Others have argued that it can be meaningful. But lacking a rigorous statistical foundation, the analysis of disagreement patterns can resemble a high-tech form of tea-leaf-reading. We contribute a framework for analyzing the variation of per-item annotator response distributions to data for humans-in-the-loop machine learning. We provide visualizations for, and use the framework to analyze the variance in, a crowdsourced dataset of hard-to-classify examples from the OpenImages archive.",
}
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%0 Conference Proceedings
%T Annotator Response Distributions as a Sampling Frame
%A Homan, Christopher
%A Weerasooriya, Tharindu Cyril
%A Aroyo, Lora
%A Welty, Chris
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Tonelli, Sara
%Y Rieser, Verena
%Y Uma, Alexandra
%S Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F homan-etal-2022-annotator
%X Annotator disagreement is often dismissed as noise or the result of poor annotation process quality. Others have argued that it can be meaningful. But lacking a rigorous statistical foundation, the analysis of disagreement patterns can resemble a high-tech form of tea-leaf-reading. We contribute a framework for analyzing the variation of per-item annotator response distributions to data for humans-in-the-loop machine learning. We provide visualizations for, and use the framework to analyze the variance in, a crowdsourced dataset of hard-to-classify examples from the OpenImages archive.
%U https://aclanthology.org/2022.nlperspectives-1.8
%P 56-65
Markdown (Informal)
[Annotator Response Distributions as a Sampling Frame](https://aclanthology.org/2022.nlperspectives-1.8) (Homan et al., NLPerspectives 2022)
ACL
- Christopher Homan, Tharindu Cyril Weerasooriya, Lora Aroyo, and Chris Welty. 2022. Annotator Response Distributions as a Sampling Frame. In Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022, pages 56–65, Marseille, France. European Language Resources Association.