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Responding with multi-modal content has been recognized as an essentialcapability for an intelligent conversational agent. In this paper, we introducethe MMDialog dataset to better facilitate multi-modal conversation. MMDialog iscomposed of a curated set of 1.08 million real-world dialogues with 1.53million unique images across 4,184 topics. MMDialog has two main and uniqueadvantages. First, it is the largest multi-modal conversation dataset by thenumber of dialogues by 88x. Second, it contains massive topics to generalizethe open-domain. To build engaging dialogue system with this dataset, wepropose and normalize two response producing tasks based on retrieval andgenerative scenarios. In addition, we build two baselines for above tasks withstate-of-the-art techniques and report their experimental performance. We alsopropose a novel evaluation metric MM-Relevance to measure the multi-modalresponses. Our dataset and scripts are available inhttps://github.com/victorsungo/MMDialog.

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation