  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.
