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        <title>Cross-document Event Coreference Search: Task, Dataset and Modeling</title>
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        <namePart type="given">Alon</namePart>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task – Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreferecene dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.</abstract>
    <identifier type="citekey">eirew-etal-2022-cross</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.58</identifier>
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%0 Conference Proceedings
%T Cross-document Event Coreference Search: Task, Dataset and Modeling
%A Eirew, Alon
%A Caciularu, Avi
%A Dagan, Ido
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F eirew-etal-2022-cross
%X The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task – Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreferecene dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.
%R 10.18653/v1/2022.emnlp-main.58
%U https://aclanthology.org/2022.emnlp-main.58/
%U https://doi.org/10.18653/v1/2022.emnlp-main.58
%P 900-913

Markdown (Informal)

[Cross-document Event Coreference Search: Task, Dataset and Modeling](https://aclanthology.org/2022.emnlp-main.58/) (Eirew et al., EMNLP 2022)

• Cross-document Event Coreference Search: Task, Dataset and Modeling (Eirew et al., EMNLP 2022)

ACL

• Alon Eirew, Avi Caciularu, and Ido Dagan. 2022. Cross-document Event Coreference Search: Task, Dataset and Modeling. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 900–913, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.