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        <title>Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories</title>
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        <namePart type="given">Hikaru</namePart>
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    <abstract>This paper presents Text2Traj2Text, a novel learning-by-synthesis framework for captioning possible contexts behind shopper’s trajectory data in retail stores. Our work will impact various retail applications that need better customer understanding, such as targeted advertising and inventory management. The key idea is leveraging large language models to synthesize a diverse and realistic collection of contextual captions as well as the corresponding movement trajectories on a store map. Despite learned from fully synthesized data, the captioning model can generalize well to trajectories/captions created by real human subjects. Our systematic evaluation confirmed the effectiveness of the proposed framework over competitive approaches in terms of ROUGE and BERT Score metrics.</abstract>
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%0 Conference Proceedings
%T Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories
%A Asano, Hikaru
%A Yonetani, Ryo
%A Sekii, Taiki
%A Ouchi, Hiroki
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F asano-etal-2024-text2traj2text-learning
%X This paper presents Text2Traj2Text, a novel learning-by-synthesis framework for captioning possible contexts behind shopper’s trajectory data in retail stores. Our work will impact various retail applications that need better customer understanding, such as targeted advertising and inventory management. The key idea is leveraging large language models to synthesize a diverse and realistic collection of contextual captions as well as the corresponding movement trajectories on a store map. Despite learned from fully synthesized data, the captioning model can generalize well to trajectories/captions created by real human subjects. Our systematic evaluation confirmed the effectiveness of the proposed framework over competitive approaches in terms of ROUGE and BERT Score metrics.
%U https://aclanthology.org/2024.inlg-main.24
%P 289-302

Markdown (Informal)

[Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories](https://aclanthology.org/2024.inlg-main.24) (Asano et al., INLG 2024)

• Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories (Asano et al., INLG 2024)

ACL

• Hikaru Asano, Ryo Yonetani, Taiki Sekii, and Hiroki Ouchi. 2024. Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories. In Proceedings of the 17th International Natural Language Generation Conference, pages 289–302, Tokyo, Japan. Association for Computational Linguistics.