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        <namePart type="given">Nasrin</namePart>
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    <name type="personal">
        <namePart type="given">Jianfeng</namePart>
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        <namePart type="given">Georgios</namePart>
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    <abstract>The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.</abstract>
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%0 Conference Proceedings
%T Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
%A Mostafazadeh, Nasrin
%A Brockett, Chris
%A Dolan, Bill
%A Galley, Michel
%A Gao, Jianfeng
%A Spithourakis, Georgios
%A Vanderwende, Lucy
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F mostafazadeh-etal-2017-image
%X The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.
%U https://aclanthology.org/I17-1047/
%P 462-472

Markdown (Informal)

[Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation](https://aclanthology.org/I17-1047/) (Mostafazadeh et al., IJCNLP 2017)

• Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation (Mostafazadeh et al., IJCNLP 2017)

ACL

• Nasrin Mostafazadeh, Chris Brockett, Bill Dolan, Michel Galley, Jianfeng Gao, Georgios Spithourakis, and Lucy Vanderwende. 2017. Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 462–472, Taipei, Taiwan. Asian Federation of Natural Language Processing.