@article{rodriguez-barroso-etal-2024-federated,
title = "Federated Learning for Exploiting Annotators{'} Disagreements in Natural Language Processing",
author = "Rodr{\'\i}guez-Barroso, Nuria and
C{\'a}mara, Eugenio Mart{\'\i}nez and
Collados, Jose Camacho and
Luz{\'o}n, M. Victoria and
Herrera, Francisco",
journal = "Transactions of the Association for Computational Linguistics",
volume = "12",
year = "2024",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2024.tacl-1.35",
doi = "10.1162/tacl_a_00664",
pages = "630--648",
abstract = "The annotation of ambiguous or subjective NLP tasks is usually addressed by various annotators. In most datasets, these annotations are aggregated into a single ground truth. However, this omits divergent opinions of annotators, hence missing individual perspectives. We propose FLEAD (Federated Learning for Exploiting Annotators{'} Disagreements), a methodology built upon federated learning to independently learn from the opinions of all the annotators, thereby leveraging all their underlying information without relying on a single ground truth. We conduct an extensive experimental study and analysis in diverse text classification tasks to show the contribution of our approach with respect to mainstream approaches based on majority voting and other recent methodologies that also learn from annotator disagreements.",
}
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<abstract>The annotation of ambiguous or subjective NLP tasks is usually addressed by various annotators. In most datasets, these annotations are aggregated into a single ground truth. However, this omits divergent opinions of annotators, hence missing individual perspectives. We propose FLEAD (Federated Learning for Exploiting Annotators’ Disagreements), a methodology built upon federated learning to independently learn from the opinions of all the annotators, thereby leveraging all their underlying information without relying on a single ground truth. We conduct an extensive experimental study and analysis in diverse text classification tasks to show the contribution of our approach with respect to mainstream approaches based on majority voting and other recent methodologies that also learn from annotator disagreements.</abstract>
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%0 Journal Article
%T Federated Learning for Exploiting Annotators’ Disagreements in Natural Language Processing
%A Rodríguez-Barroso, Nuria
%A Cámara, Eugenio Martínez
%A Collados, Jose Camacho
%A Luzón, M. Victoria
%A Herrera, Francisco
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F rodriguez-barroso-etal-2024-federated
%X The annotation of ambiguous or subjective NLP tasks is usually addressed by various annotators. In most datasets, these annotations are aggregated into a single ground truth. However, this omits divergent opinions of annotators, hence missing individual perspectives. We propose FLEAD (Federated Learning for Exploiting Annotators’ Disagreements), a methodology built upon federated learning to independently learn from the opinions of all the annotators, thereby leveraging all their underlying information without relying on a single ground truth. We conduct an extensive experimental study and analysis in diverse text classification tasks to show the contribution of our approach with respect to mainstream approaches based on majority voting and other recent methodologies that also learn from annotator disagreements.
%R 10.1162/tacl_a_00664
%U https://aclanthology.org/2024.tacl-1.35
%U https://doi.org/10.1162/tacl_a_00664
%P 630-648
Markdown (Informal)
[Federated Learning for Exploiting Annotators’ Disagreements in Natural Language Processing](https://aclanthology.org/2024.tacl-1.35) (Rodríguez-Barroso et al., TACL 2024)
ACL