The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service

Understand

Human conversations are complicated and building a human-like dialogue agent is an extremely challenging task.

• With the rapid development of deep learning techniques, data-driven models become more and more prevalent which need a huge amount of real conversation data.
• In this paper, we construct a large-scale real scenario Chinese E-commerce conversation corpus, JDDC, with more than 1 million multi-turn dialogues, 20 million utterances, and 150 million words.
• The dataset reflects several characteristics of human-human conversations, e.g., goal-driven, and long-term dependency among the context.