Abstract
To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014). In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019). Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215 possible style traits. Automatic metrics and human evaluations of engagingness show the efficacy of our approach; in particular, we obtain state-of-the-art performance on the existing IGC task, and our best performing model is almost on par with humans on the Image-Chat test set (preferred 47.7% of the time).- Anthology ID:
- 2020.acl-main.219
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2414–2429
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.219
- DOI:
- 10.18653/v1/2020.acl-main.219
- Cite (ACL):
- Kurt Shuster, Samuel Humeau, Antoine Bordes, and Jason Weston. 2020. Image-Chat: Engaging Grounded Conversations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2414–2429, Online. Association for Computational Linguistics.
- Cite (Informal):
- Image-Chat: Engaging Grounded Conversations (Shuster et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.219.pdf