Enhanced Labelling in Active Learning for Coreference Resolution

Vebjørn Espeland, Beatrice Alex, Benjamin Bach


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.
Anthology ID:
2020.crac-1.12
Volume:
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference
Month:
December
Year:
2020
Address:
Barcelona, Spain (online)
Editors:
Maciej Ogrodniczuk, Vincent Ng, Yulia Grishina, Sameer Pradhan
Venue:
CRAC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
111–121
Language:
URL:
https://aclanthology.org/2020.crac-1.12
DOI:
Bibkey:
Cite (ACL):
Vebjørn Espeland, Beatrice Alex, and Benjamin Bach. 2020. Enhanced Labelling in Active Learning for Coreference Resolution. In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, pages 111–121, Barcelona, Spain (online). Association for Computational Linguistics.
Cite (Informal):
Enhanced Labelling in Active Learning for Coreference Resolution (Espeland et al., CRAC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.crac-1.12.pdf
Data
CoNLL-2012