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        <title>Incorporating Textual Evidence in Visual Storytelling</title>
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        <namePart type="given">Tianyi</namePart>
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        <namePart type="given">Sujian</namePart>
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            <title>Proceedings of the 1st Workshop on Discourse Structure in Neural NLG</title>
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        <name type="personal">
            <namePart type="given">Anusha</namePart>
            <namePart type="family">Balakrishnan</namePart>
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            <namePart type="given">Chandra</namePart>
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    <abstract>Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8102</identifier>
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        <date>2019-11</date>
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            <start>13</start>
            <end>17</end>
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%0 Conference Proceedings
%T Incorporating Textual Evidence in Visual Storytelling
%A Li, Tianyi
%A Li, Sujian
%Y Balakrishnan, Anusha
%Y Demberg, Vera
%Y Khatri, Chandra
%Y Rastogi, Abhinav
%Y Scott, Donia
%Y Walker, Marilyn
%Y White, Michael
%S Proceedings of the 1st Workshop on Discourse Structure in Neural NLG
%D 2019
%8 November
%I Association for Computational Linguistics
%C Tokyo, Japan
%F li-li-2019-incorporating
%X Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering.
%R 10.18653/v1/W19-8102
%U https://aclanthology.org/W19-8102
%U https://doi.org/10.18653/v1/W19-8102
%P 13-17

Markdown (Informal)

[Incorporating Textual Evidence in Visual Storytelling](https://aclanthology.org/W19-8102) (Li & Li, INLG 2019)

• Incorporating Textual Evidence in Visual Storytelling (Li & Li, INLG 2019)

ACL

• Tianyi Li and Sujian Li. 2019. Incorporating Textual Evidence in Visual Storytelling. In Proceedings of the 1st Workshop on Discourse Structure in Neural NLG, pages 13–17, Tokyo, Japan. Association for Computational Linguistics.