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        <title>Beyond Pairwise: Global Zero-shot Temporal Graph Generation</title>
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        <namePart type="given">Alon</namePart>
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        <namePart type="given">Kfir</namePart>
        <namePart type="family">Bar</namePart>
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    <name type="personal">
        <namePart type="given">Ido</namePart>
        <namePart type="family">Dagan</namePart>
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            <title>Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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        <name type="personal">
            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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        <name type="personal">
            <namePart type="given">Carolyn</namePart>
            <namePart type="family">Rose</namePart>
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        <name type="personal">
            <namePart type="given">Violet</namePart>
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    <abstract>Temporal relation extraction (TRE) is a fundamental task in natural language processing (NLP) that involves identifying the temporal relationships between events in a document. Despite the advances in large language models (LLMs), their application to TRE remains limited. Most existing approaches rely on pairwise classification, where event pairs are classified in isolation, leading to computational inefficiency and a lack of global consistency in the resulting temporal graph. In this work, we propose a novel zero-shot method for TRE that generates a document’s complete temporal graph in a single step, followed by temporal constraint optimization to refine predictions and enforce temporal consistency across relations. Additionally, we introduce OmniTemp, a new dataset with complete annotations for all pairs of targeted events within a document. Through experiments and analyses, we demonstrate that our method outperforms existing zero-shot approaches and offers a competitive alternative to supervised TRE models.</abstract>
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%0 Conference Proceedings
%T Beyond Pairwise: Global Zero-shot Temporal Graph Generation
%A Eirew, Alon
%A Bar, Kfir
%A Dagan, Ido
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F eirew-etal-2025-beyond
%X Temporal relation extraction (TRE) is a fundamental task in natural language processing (NLP) that involves identifying the temporal relationships between events in a document. Despite the advances in large language models (LLMs), their application to TRE remains limited. Most existing approaches rely on pairwise classification, where event pairs are classified in isolation, leading to computational inefficiency and a lack of global consistency in the resulting temporal graph. In this work, we propose a novel zero-shot method for TRE that generates a document’s complete temporal graph in a single step, followed by temporal constraint optimization to refine predictions and enforce temporal consistency across relations. Additionally, we introduce OmniTemp, a new dataset with complete annotations for all pairs of targeted events within a document. Through experiments and analyses, we demonstrate that our method outperforms existing zero-shot approaches and offers a competitive alternative to supervised TRE models.
%U https://aclanthology.org/2025.emnlp-main.1601/
%P 31428-31446

Markdown (Informal)

[Beyond Pairwise: Global Zero-shot Temporal Graph Generation](https://aclanthology.org/2025.emnlp-main.1601/) (Eirew et al., EMNLP 2025)

• Beyond Pairwise: Global Zero-shot Temporal Graph Generation (Eirew et al., EMNLP 2025)

ACL

• Alon Eirew, Kfir Bar, and Ido Dagan. 2025. Beyond Pairwise: Global Zero-shot Temporal Graph Generation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 31428–31446, Suzhou, China. Association for Computational Linguistics.