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        <title>Multimodal Event Transformer for Image-guided Story Ending Generation</title>
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    <name type="personal">
        <namePart type="given">Yucheng</namePart>
        <namePart type="family">Zhou</namePart>
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    <name type="personal">
        <namePart type="given">Guodong</namePart>
        <namePart type="family">Long</namePart>
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            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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    <abstract>Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but overlook reasoning and mining implicit information from story plots and ending image. To tackle this drawback, we propose a multimodal event transformer, an event-based reasoning framework for IgSEG. Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality. Next, we connect visual and semantic event graphs and utilize cross-modal fusion to integrate different-modality features. In addition, we propose a multimodal injector to adaptive pass essential information to decoder. Besides, we present an incoherence detection to enhance the understanding context of a story plot and the robustness of graph modeling for our model. Experimental results show that our method achieves state-of-the-art performance for the image-guided story ending generation.</abstract>
    <identifier type="citekey">zhou-long-2023-multimodal</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.249</identifier>
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        <url>https://aclanthology.org/2023.eacl-main.249</url>
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%0 Conference Proceedings
%T Multimodal Event Transformer for Image-guided Story Ending Generation
%A Zhou, Yucheng
%A Long, Guodong
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F zhou-long-2023-multimodal
%X Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but overlook reasoning and mining implicit information from story plots and ending image. To tackle this drawback, we propose a multimodal event transformer, an event-based reasoning framework for IgSEG. Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality. Next, we connect visual and semantic event graphs and utilize cross-modal fusion to integrate different-modality features. In addition, we propose a multimodal injector to adaptive pass essential information to decoder. Besides, we present an incoherence detection to enhance the understanding context of a story plot and the robustness of graph modeling for our model. Experimental results show that our method achieves state-of-the-art performance for the image-guided story ending generation.
%R 10.18653/v1/2023.eacl-main.249
%U https://aclanthology.org/2023.eacl-main.249
%U https://doi.org/10.18653/v1/2023.eacl-main.249
%P 3434-3444

Markdown (Informal)

[Multimodal Event Transformer for Image-guided Story Ending Generation](https://aclanthology.org/2023.eacl-main.249) (Zhou & Long, EACL 2023)

• Multimodal Event Transformer for Image-guided Story Ending Generation (Zhou & Long, EACL 2023)

ACL

• Yucheng Zhou and Guodong Long. 2023. Multimodal Event Transformer for Image-guided Story Ending Generation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3434–3444, Dubrovnik, Croatia. Association for Computational Linguistics.