Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation

Nasrin Mostafazadeh, Chris Brockett, Bill Dolan, Michel Galley, Jianfeng Gao, Georgios Spithourakis, Lucy Vanderwende

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.
Anthology ID:
I17-1047
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
462–472
Language:
URL:
https://aclanthology.org/I17-1047
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation (Mostafazadeh et al., IJCNLP 2017)
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PDF:
https://preview.aclanthology.org/remove-xml-comments/I17-1047.pdf
Note:
 I17-1047.Notes.pdf
Data
FrameNet, VQG, VisDial, Visual Question Answering
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