How to Encode Domain Information in Relation Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AT6QYVT6J" target="_blank" >RIV/00216208:11320/25:T6QYVT6J - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195940749&partnerID=40&md5=819b53ce4aef81ec8446bb8025c12dee" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195940749&partnerID=40&md5=819b53ce4aef81ec8446bb8025c12dee</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
How to Encode Domain Information in Relation Classification
Original language description
Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obtain better performance is non-trivial. We explore a multi-domain training setup for RC, and attempt to improve performance by encoding domain information. Our proposed models improve > 2 Macro-F1 against the baseline setup, and our analysis reveals that not all the labels benefit the same: The classes which occupy a similar space across domains (i.e., their interpretation is close across them, for example physical) benefit the least, while domain-dependent relations (e.g., part-of) improve the most when encoding domain information. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.
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
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
Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.
ISBN
978-249381410-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
8301-8306
Publisher name
European Language Resources Association (ELRA)
Place of publication
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Event location
Torino, Italia
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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