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        <title>Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training</title>
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        <namePart type="given">Masaki</namePart>
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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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            <namePart type="given">Owen</namePart>
            <namePart type="family">Rambow</namePart>
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            <namePart type="given">Leo</namePart>
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        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Hend</namePart>
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            <namePart type="given">Barbara</namePart>
            <namePart type="given">Di</namePart>
            <namePart type="family">Eugenio</namePart>
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            <namePart type="given">Steven</namePart>
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    <abstract>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 seq2seq-based 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.</abstract>
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%0 Conference Proceedings
%T Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training
%A Asada, Masaki
%A Miwa, Makoto
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F asada-miwa-2025-improving
%X 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 seq2seq-based 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.
%U https://aclanthology.org/2025.coling-main.473/
%P 7099-7105

Markdown (Informal)

[Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training](https://aclanthology.org/2025.coling-main.473/) (Asada & Miwa, COLING 2025)

• Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training (Asada & Miwa, COLING 2025)

ACL

• Masaki Asada and Makoto Miwa. 2025. Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7099–7105, Abu Dhabi, UAE. Association for Computational Linguistics.