<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mu-li-2024-causal">
    <titleInfo>
        <title>A Causal Approach for Counterfactual Reasoning in Narratives</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Feiteng</namePart>
        <namePart type="family">Mu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wenjie</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Counterfactual reasoning in narratives requires predicting how alternative conditions, contrary to what actually happened, might have resulted in different outcomes.One major challenge is to maintain the causality between the counterfactual condition and the generated counterfactual outcome. In this paper, we propose a basic VAE module for counterfactual reasoning in narratives. We further introduce a pre-trained classifier and external event commonsense to mitigate the posterior collapse problem in the VAE approach, and improve the causality between the counterfactual condition and the generated counterfactual outcome. We evaluate our method on two public benchmarks. Experiments show that our method is effective.</abstract>
    <identifier type="citekey">mu-li-2024-causal</identifier>
    <identifier type="doi">10.18653/v1/2024.acl-long.354</identifier>
    <location>
        <url>https://aclanthology.org/2024.luhme-long.354/</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>6556</start>
            <end>6569</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Causal Approach for Counterfactual Reasoning in Narratives
%A Mu, Feiteng
%A Li, Wenjie
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F mu-li-2024-causal
%X Counterfactual reasoning in narratives requires predicting how alternative conditions, contrary to what actually happened, might have resulted in different outcomes.One major challenge is to maintain the causality between the counterfactual condition and the generated counterfactual outcome. In this paper, we propose a basic VAE module for counterfactual reasoning in narratives. We further introduce a pre-trained classifier and external event commonsense to mitigate the posterior collapse problem in the VAE approach, and improve the causality between the counterfactual condition and the generated counterfactual outcome. We evaluate our method on two public benchmarks. Experiments show that our method is effective.
%R 10.18653/v1/2024.acl-long.354
%U https://aclanthology.org/2024.luhme-long.354/
%U https://doi.org/10.18653/v1/2024.acl-long.354
%P 6556-6569

Markdown (Informal)

[A Causal Approach for Counterfactual Reasoning in Narratives](https://aclanthology.org/2024.luhme-long.354/) (Mu & Li, ACL 2024)

• A Causal Approach for Counterfactual Reasoning in Narratives (Mu & Li, ACL 2024)

ACL

• Feiteng Mu and Wenjie Li. 2024. A Causal Approach for Counterfactual Reasoning in Narratives. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6556–6569, Bangkok, Thailand. Association for Computational Linguistics.