End-to-end Multilingual Coreference Resolution with Headword Mention Representation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973277" target="_blank" >RIV/49777513:23520/24:43973277 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.crac-1.10" target="_blank" >https://aclanthology.org/2024.crac-1.10</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
End-to-end Multilingual Coreference Resolution with Headword Mention Representation
Original language description
This paper describes our approach to the CRAC 2024 Shared Task on Multilingual Coreference Resolution. Our model is based on an endto-end coreference resolution system. Apart from joined multilingual training, we improved our results with headword mention representation and training large model mT5-xxl through LORA. We provide an analysis of the performance of our model. Our system ended up in 4th place. Moreover, we reached the best performance on three datasets out of 21.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of The Seventh Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2024)
ISBN
979-8-89176-171-1
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
107-113
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg
Event location
Miami
Event date
Nov 15, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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