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    <abstract>This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction.</abstract>
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%0 Conference Proceedings
%T The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue
%A Haber, Janosch
%A Baumgärtner, Tim
%A Takmaz, Ece
%A Gelderloos, Lieke
%A Bruni, Elia
%A Fernández, Raquel
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F haber-etal-2019-photobook
%X This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction.
%R 10.18653/v1/P19-1184
%U https://aclanthology.org/P19-1184
%U https://doi.org/10.18653/v1/P19-1184
%P 1895-1910

Markdown (Informal)

[The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue](https://aclanthology.org/P19-1184) (Haber et al., ACL 2019)

• The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue (Haber et al., ACL 2019)

ACL

• Janosch Haber, Tim Baumgärtner, Ece Takmaz, Lieke Gelderloos, Elia Bruni, and Raquel Fernández. 2019. The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1895–1910, Florence, Italy. Association for Computational Linguistics.