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

TL;DR

The paper introduces MMDialog, the largest multi-modal dialogue dataset with over a million dialogues and images, enabling more general and engaging open-domain multi-modal conversations, along with baseline models and a new evaluation metric.

Contribution

It provides the first large-scale multi-modal dialogue dataset with diverse topics and baseline models for retrieval and generative tasks.

Findings

MMDialog is 88 times larger than previous datasets.

Baseline models demonstrate the feasibility of multi-modal dialogue generation.

The MM-Relevance metric effectively evaluates multi-modal responses.

Abstract

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.