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        <title>Weakly-Supervised Questions for Zero-Shot Relation Extraction</title>
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        <namePart type="given">Saeed</namePart>
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            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
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    <abstract>Zero-Shot Relation Extraction (ZRE) is the task of Relation Extraction where the training and test sets have no shared relation types. This very challenging domain is a good test of a model’s ability to generalize. Previous approaches to ZRE reframed relation extraction as Question Answering (QA), allowing for the use of pre-trained QA models. However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. Our technique can successfully translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. On tail entity extraction, we outperform the previous state-of-the-art by more than 16 F1 points without using gold question templates. On the RE-QA dataset where no previous baseline for relation extraction exists, our proposed algorithm comes within 0.7 F1 points of a system that uses gold question templates. Our model also outperforms the state-of-the-art ZRE baselines on the FewRel and WikiZSL datasets, showing that QA models no longer need template questions to match the performance of models specifically tailored to the ZRE task. Our implementation is available at https://github.com/fyshelab/QA-ZRE.</abstract>
    <identifier type="citekey">najafi-fyshe-2023-weakly</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.224</identifier>
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        <date>2023-05</date>
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            <start>3075</start>
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%0 Conference Proceedings
%T Weakly-Supervised Questions for Zero-Shot Relation Extraction
%A Najafi, Saeed
%A Fyshe, Alona
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F najafi-fyshe-2023-weakly
%X Zero-Shot Relation Extraction (ZRE) is the task of Relation Extraction where the training and test sets have no shared relation types. This very challenging domain is a good test of a model’s ability to generalize. Previous approaches to ZRE reframed relation extraction as Question Answering (QA), allowing for the use of pre-trained QA models. However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. Our technique can successfully translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. On tail entity extraction, we outperform the previous state-of-the-art by more than 16 F1 points without using gold question templates. On the RE-QA dataset where no previous baseline for relation extraction exists, our proposed algorithm comes within 0.7 F1 points of a system that uses gold question templates. Our model also outperforms the state-of-the-art ZRE baselines on the FewRel and WikiZSL datasets, showing that QA models no longer need template questions to match the performance of models specifically tailored to the ZRE task. Our implementation is available at https://github.com/fyshelab/QA-ZRE.
%R 10.18653/v1/2023.eacl-main.224
%U https://aclanthology.org/2023.eacl-main.224
%U https://doi.org/10.18653/v1/2023.eacl-main.224
%P 3075-3087

Markdown (Informal)

[Weakly-Supervised Questions for Zero-Shot Relation Extraction](https://aclanthology.org/2023.eacl-main.224) (Najafi & Fyshe, EACL 2023)

• Weakly-Supervised Questions for Zero-Shot Relation Extraction (Najafi & Fyshe, EACL 2023)

ACL

• Saeed Najafi and Alona Fyshe. 2023. Weakly-Supervised Questions for Zero-Shot Relation Extraction. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3075–3087, Dubrovnik, Croatia. Association for Computational Linguistics.