CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos

• URL: http://arxiv.org/abs/2303.09713v2
• Date: Wed, 16 Aug 2023 08:17:02 GMT
• Title: CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos
• Authors: Seungju Han, Jack Hessel, Nouha Dziri, Yejin Choi, Youngjae Yu
• Abstract summary: We introduce CHAMPAGNE, a generative model of conversations that can account for visual contexts. To train CHAMPAGNE, we collect and release-18M, a large-scale corpus of 18M video-based dialogues.
• Score: 75.37313546008639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual information is central to conversation: body gestures and physical behaviour, for example, contribute to meaning that transcends words alone. To date, however, most neural conversational models are limited to just text. We introduce CHAMPAGNE, a generative model of conversations that can account for visual contexts. To train CHAMPAGNE, we collect and release YTD-18M, a large-scale corpus of 18M video-based dialogues. YTD-18M is constructed from web videos: crucial to our data collection pipeline is a pretrained language model that converts error-prone automatic transcripts to a cleaner dialogue format while maintaining meaning. Human evaluation reveals that YTD-18M is more sensible and specific than prior resources (MMDialog, 1M dialogues), while maintaining visual-groundedness. Experiments demonstrate that 1) CHAMPAGNE learns to conduct conversation from YTD-18M; and 2) when fine-tuned, it achieves state-of-the-art results on four vision-language tasks focused on real-world conversations. We release data, models, and code.

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