Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AD9X7IJRX" target="_blank" >RIV/00216208:11320/26:D9X7IJRX - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training
Original language description
Relation extraction is a crucial natural language processing task that extracts relational triplets from raw text. Syntactic dependencies information has shown its effectiveness for relation extraction tasks. However, in most existing studies, dependency information is used only for traditional encoder-only-based relation extraction, not for generative sequence-to-sequence (seq2seq)-based relation extraction. In this study, we propose a syntax-aware seq2seq pre-trained model for seq2seq-based relation extraction. The model incorporates dependency information into a seq2seq pre-trained language model by continual pre-training with a seq2seqbased dependency parsing task. Experimental results on two widely used relation extraction benchmark datasets show that dependency parsing pre-training can improve the relation extraction performance. © 2025 Association for Computational Linguistics.
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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proc. Main Conf. Int. Conf. Comput. Linguist., COLING
ISBN
979-8-89176-196-4
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
7099-7105
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
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Event location
Abu Dhabi
Event date
Jan 1, 2026
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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