Multilingual Coreference Resolution with Harmonized Annotations

Ondřej Pražák, Miloslav Konopík, Jakub Sido


Abstract
In this paper, we present coreference resolution experiments with a newly created multilingual corpus CorefUD (Nedoluzhko et al.,2021). We focus on the following languages: Czech, Russian, Polish, German, Spanish, and Catalan. In addition to monolingual experiments, we combine the training data in multilingual experiments and train two joined models - for Slavic languages and for all the languages together. We rely on an end-to-end deep learning model that we slightly adapted for the CorefUD corpus. Our results show that we can profit from harmonized annotations, and using joined models helps significantly for the languages with smaller training data.
Anthology ID:
2021.ranlp-1.125
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1119–1123
Language:
URL:
https://aclanthology.org/2021.ranlp-1.125
DOI:
Bibkey:
Cite (ACL):
Ondřej Pražák, Miloslav Konopík, and Jakub Sido. 2021. Multilingual Coreference Resolution with Harmonized Annotations. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1119–1123, Held Online. INCOMA Ltd..
Cite (Informal):
Multilingual Coreference Resolution with Harmonized Annotations (Pražák et al., RANLP 2021)
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PDF:
https://aclanthology.org/2021.ranlp-1.125.pdf