Federated Learning for Exploiting Annotators’ Disagreements in Natural Language Processing Rodríguez Barroso, Nuria Martínez Cámara, Eugenio Camacho Collados, Jose Luzón, M. Victoria Herrera Triguero, Francisco 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. 2024-07-31T09:45:31Z 2024-07-31T09:45:31Z 2024-05-16 journal article Rodríguez Barroso, N. et. al. vol. 12, pp. 630–648, 2024. [https://doi.org/10.1162/tacl_a_00664] https://hdl.handle.net/10481/93687 10.1162/tacl_a_00664 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MIT Press Direct