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        <title>Incorporating Factuality Inference to Identify Document-level Event Factuality</title>
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        <namePart type="given">Heng</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Xiaoxu</namePart>
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    <abstract>Document-level Event Factuality Identification (DEFI) refers to identifying the degree of certainty that a specific event occurs in a document. Previous studies on DEFI failed to link the document-level event factuality with various sentence-level factuality values in the same document. In this paper, we innovatively propose an event factuality inference task to bridge the sentence-level and the document-level event factuality semantically. Specifically, we present a Sentence-to-Document Inference Network (SDIN) that contains a multi-layer interaction module and a gated aggregation module to integrate the above two tasks, and employ a multi-task learning framework to improve the performance of DEFI. The experimental results on the public English and Chinese DLEF datasets show that our model outperforms the SOTA baselines significantly.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.879</identifier>
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%0 Conference Proceedings
%T Incorporating Factuality Inference to Identify Document-level Event Factuality
%A Zhang, Heng
%A Li, Peifeng
%A Qian, Zhong
%A Zhu, Xiaoxu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-incorporating
%X Document-level Event Factuality Identification (DEFI) refers to identifying the degree of certainty that a specific event occurs in a document. Previous studies on DEFI failed to link the document-level event factuality with various sentence-level factuality values in the same document. In this paper, we innovatively propose an event factuality inference task to bridge the sentence-level and the document-level event factuality semantically. Specifically, we present a Sentence-to-Document Inference Network (SDIN) that contains a multi-layer interaction module and a gated aggregation module to integrate the above two tasks, and employ a multi-task learning framework to improve the performance of DEFI. The experimental results on the public English and Chinese DLEF datasets show that our model outperforms the SOTA baselines significantly.
%R 10.18653/v1/2023.findings-acl.879
%U https://aclanthology.org/2023.findings-acl.879
%U https://doi.org/10.18653/v1/2023.findings-acl.879
%P 13990-14002

Markdown (Informal)

[Incorporating Factuality Inference to Identify Document-level Event Factuality](https://aclanthology.org/2023.findings-acl.879) (Zhang et al., Findings 2023)

• Incorporating Factuality Inference to Identify Document-level Event Factuality (Zhang et al., Findings 2023)

ACL

• Heng Zhang, Peifeng Li, Zhong Qian, and Xiaoxu Zhu. 2023. Incorporating Factuality Inference to Identify Document-level Event Factuality. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13990–14002, Toronto, Canada. Association for Computational Linguistics.