<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cao-etal-2021-uncertain">
    <titleInfo>
        <title>Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pengfei</namePart>
        <namePart type="family">Cao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yubo</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yuqing</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kang</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jun</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Event factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event. To this end, we study the problem of document-level event factuality identification, which determines the event factuality from the view of a document. For this task, we need to consider two important characteristics: Local Uncertainty and Global Structure, which can be utilized to improve performance. In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics. Specifically, we devise a Local Uncertainty Estimation module to model the uncertainty of local information. Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information. Experimental results demonstrate the effectiveness of our proposed method, outperforming the previous state-of-the-art model by 8.4% and 11.45% of F1 score on two widely used datasets.</abstract>
    <identifier type="citekey">cao-etal-2021-uncertain</identifier>
    <identifier type="doi">10.18653/v1/2021.emnlp-main.207</identifier>
    <location>
        <url>https://aclanthology.org/2021.emnlp-main.207</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>2636</start>
            <end>2645</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification
%A Cao, Pengfei
%A Chen, Yubo
%A Yang, Yuqing
%A Liu, Kang
%A Zhao, Jun
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F cao-etal-2021-uncertain
%X Event factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event. To this end, we study the problem of document-level event factuality identification, which determines the event factuality from the view of a document. For this task, we need to consider two important characteristics: Local Uncertainty and Global Structure, which can be utilized to improve performance. In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics. Specifically, we devise a Local Uncertainty Estimation module to model the uncertainty of local information. Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information. Experimental results demonstrate the effectiveness of our proposed method, outperforming the previous state-of-the-art model by 8.4% and 11.45% of F1 score on two widely used datasets.
%R 10.18653/v1/2021.emnlp-main.207
%U https://aclanthology.org/2021.emnlp-main.207
%U https://doi.org/10.18653/v1/2021.emnlp-main.207
%P 2636-2645

Markdown (Informal)

[Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification](https://aclanthology.org/2021.emnlp-main.207) (Cao et al., EMNLP 2021)

• Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification (Cao et al., EMNLP 2021)

ACL

• Pengfei Cao, Yubo Chen, Yuqing Yang, Kang Liu, and Jun Zhao. 2021. Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2636–2645, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.