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        <title>RoViST: Learning Robust Metrics for Visual Storytelling</title>
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        <namePart type="given">Eileen</namePart>
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    <abstract>Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have poor correlation with human evaluation scores and do not explicitly consider other criteria necessary for storytelling such as sentence structure or topic coherence. Moreover, a single score is not enough to assess a story as it does not inform us about what specific errors were made by the model. In this paper, we propose 3 evaluation metrics sets that analyses which aspects we would look for in a good story: 1) visual grounding, 2) coherence, and 3) non-redundancy. We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST). Our metric sets outperforms other metrics on human correlation, and could be served as a learning based evaluation metric set that is complementary to existing rule-based metrics.</abstract>
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%0 Conference Proceedings
%T RoViST: Learning Robust Metrics for Visual Storytelling
%A Wang, Eileen
%A Han, Caren
%A Poon, Josiah
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F wang-etal-2022-rovist
%X Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have poor correlation with human evaluation scores and do not explicitly consider other criteria necessary for storytelling such as sentence structure or topic coherence. Moreover, a single score is not enough to assess a story as it does not inform us about what specific errors were made by the model. In this paper, we propose 3 evaluation metrics sets that analyses which aspects we would look for in a good story: 1) visual grounding, 2) coherence, and 3) non-redundancy. We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST). Our metric sets outperforms other metrics on human correlation, and could be served as a learning based evaluation metric set that is complementary to existing rule-based metrics.
%R 10.18653/v1/2022.findings-naacl.206
%U https://aclanthology.org/2022.findings-naacl.206/
%U https://doi.org/10.18653/v1/2022.findings-naacl.206
%P 2691-2702

Markdown (Informal)

[RoViST: Learning Robust Metrics for Visual Storytelling](https://aclanthology.org/2022.findings-naacl.206/) (Wang et al., Findings 2022)

• RoViST: Learning Robust Metrics for Visual Storytelling (Wang et al., Findings 2022)

ACL

• Eileen Wang, Caren Han, and Josiah Poon. 2022. RoViST: Learning Robust Metrics for Visual Storytelling. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2691–2702, Seattle, United States. Association for Computational Linguistics.