Multi-modal Dialogue Generation

MMDialog is a large-scale multi-turn dialogue dataset containing multi-modal open-domain conversations derived from real human-human chat content in social media. MMDialog contains 1.08M dialogue sessions and 1.53M associated images. On average, one dialogue session has 2.59 images, which can be located anywhere at any conversation turn. Source: MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation Image Source: https://arxiv.org/pdf/2211.05719v1.pdf

• A large scale Chinese multi-modal dialogue corpus (120.84K dialogues and 198.82 K images). - MMCHAT contains image-grounded dialogues collected from real conversations on social media. - We manually annotate 100K dialogues from MMCHAT with the dialogue quality and whether the dialogues are related to the given image. - We provide the rule-filtered raw dialogues that are used to create MMChat (Rule Filtered Raw MMChat). It contains 4.257 M dialogue sessions and 4.874 M images - We provide a version of MMChat that is filtered based on LCCC (LCCC Filtered MMChat). This version contain much cleaner dialogues (492.6 K dialogue sessions and 1.066 M images)

OpenViDial 2.0 is a larger-scale open-domain multi-modal dialogue dataset compared to the previous version OpenViDial 1.0. OpenViDial 2.0 contains a total number of 5.6 million dialogue turns extracted from either movies or TV series from different resources, and each dialogue turn is paired with its corresponding visual context. Image source: https://github.com/ShannonAI/OpenViDial