Enhanced labelling in active learning for coreference resolution

Vebjørn Espeland, Benjamin Bach, Beatrice Alex

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we describe our attempt to increase the amount of information that can be retrieved through active learning sessions compared to previous approaches. We optimise the annotator’s labelling process using active learning in the context of coreference resolution. Using simulated active learning experiments, we suggest three adjustments to ensure the labelling time is spent as efficiently as possible. All three adjustments provide more information to the machine learner than the baseline, though a large impact on the F1 score over time is not observed. Compared to previous models, we report a marginal F1 improvement on the final coreference models trained using for two out of the three approaches tested when applied to the English OntoNotes 2012 Coreference Resolution data. Our best-performing model achieves 58.01 F1, an increase of 0.93 F1 over the baseline model.
Original languageEnglish
Title of host publicationProceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference 2020
PublisherAssociation for Computational Linguistics
Pages111-121
Number of pages11
Publication statusPublished - 12 Dec 2020
EventThird Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2020) at COLING 2020 -
Duration: 12 Dec 202012 Dec 2020

Workshop

WorkshopThird Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2020) at COLING 2020
Period12/12/2012/12/20

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