MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
This dataset addresses the problem of limited data for multi-modal open-domain conversation research, enabling more robust dialogue systems, though it is incremental as it builds on existing multi-modal dialogue efforts.
The authors tackled the lack of large-scale multi-modal dialogue datasets by introducing MMDialog, a dataset with 1.08 million dialogues and 1.53 million images across 4,184 topics, which is 88x larger than previous datasets, and they proposed tasks and baselines for building engaging dialogue systems.
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.