With the development of the Internet, more and more people get accustomed to
online shopping. When communicating with customer service, users may express
their requirements by means of text, images, and videos, which precipitates the
need for understanding these multimodal information for automatic customer
service systems. Images usually act as discriminators for product models, or
indicators of product failures, which play important roles in the E-commerce
scenario. On the other hand, detailed information provided by the images is
limited, and typically, customer service systems cannot understand the intents
of users without the input text. Thus, bridging the gap of the image and text
is crucial for the multimodal dialogue task. To handle this problem, we
construct JDDC 2.0, a large-scale multimodal multi-turn dialogue dataset
collected from a mainstream Chinese E-commerce platform (JD.com), containing
about 246 thousand dialogue sessions, 3 million utterances, and 507 thousand
images, along with product knowledge bases and image category annotations. We
present the solutions of top-5 teams participating in the JDDC multimodal
dialogue challenge based on this dataset, which provides valuable insights for
further researches on the multimodal dialogue task.

为了解决多模态对话中图像与文本之间的差距，我们构建了一个包含图像、产品知识库和文本的大规模数据集 JDDC 2.0，并给出了基于此数据集的顶尖团队的解决方案，为进一步研究多模态对话任务提供了有价值的启示。