Investigating Multilingual Coreference Resolution by Universal Annotations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A2U5BUUQE" target="_blank" >RIV/00216208:11320/23:2U5BUUQE - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.findings-emnlp.671/" target="_blank" >https://aclanthology.org/2023.findings-emnlp.671/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2023.findings-emnlp.671" target="_blank" >10.18653/v1/2023.findings-emnlp.671</a>
Alternative languages
Result language
angličtina
Original language name
Investigating Multilingual Coreference Resolution by Universal Annotations
Original language description
"Multilingual coreference resolution (MCR) has been a long-standing and challenging task. With the newly proposed multilingual coreference dataset, CorefUD (Nedoluzhko et al., 2022), we conduct an investigation into the task by using its harmonized universal morphosyntactic and coreference annotations. First, we study coreference by examining the ground truth data at different linguistic levels, namely mention, entity and document levels, and across different genres, to gain insights into the characteristics of coreference across multiple languages. Second, we perform an error analysis of the most challenging cases that the SotA system fails to resolve in the CRAC 2022 shared task using the universal annotations. Last, based on this analysis, we extract features from universal morphosyntactic annotations and integrate these features into a baseline system to assess their potential benefits for the MCR task. Our results show that our best configuration of features improves the baseline by 0.9% F1 score."
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
"Findings of the Association for Computational Linguistics: EMNLP 2023"
ISBN
979-8-89176-061-5
ISSN
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e-ISSN
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Number of pages
15
Pages from-to
10010-10024
Publisher name
arXiv
Place of publication
Singapore
Event location
Singapore
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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