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

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arxiv 2211.05719 v3 pith:OB5JSRVO submitted 2022-11-10 cs.CL cs.AIcs.CVcs.LGcs.MM

Jiazhan Feng , Qingfeng Sun , Can Xu , Pu Zhao , Yaming Yang , Chongyang Tao , Dongyan Zhao , Qingwei Lin This is my paper · ORCID
classification cs.CLcs.AIcs.CVcs.LGcs.MM
keywords datasetmmdialogmulti-modalconversationbuilddialoguedialoguesmillion
verification ladder T0 review T1 audit T2 compute T3 formal
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Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a curated set of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. MMDialog has two main and unique advantages. First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x. Second, it contains massive topics to generalize the open-domain. To build engaging dialogue system with this dataset, we propose and normalize two response producing tasks based on retrieval and generative scenarios. In addition, we build two baselines for above tasks with state-of-the-art techniques and report their experimental performance. We also propose a novel evaluation metric MM-Relevance to measure the multi-modal responses. Our dataset and scripts are available in https://github.com/victorsungo/MMDialog.

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Cited by 3 Pith papers

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