English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Human conversations are complicated and building a human-like dialogue agentis an extremely challenging task. With the rapid development of deep learningtechniques, data-driven models become more and more prevalent which need a hugeamount of real conversation data. In this paper, we construct a large-scalereal scenario Chinese E-commerce conversation corpus, JDDC, with more than 1million multi-turn dialogues, 20 million utterances, and 150 million words. Thedataset reflects several characteristics of human-human conversations, e.g.,goal-driven, and long-term dependency among the context. It also covers variousdialogue types including task-oriented, chitchat and question-answering. Extraintent information and three well-annotated challenge sets are also provided.Then, we evaluate several retrieval-based and generative models to providebasic benchmark performance on the JDDC corpus. And we hope JDDC can serve asan effective testbed and benefit the development of fundamental research indialogue task

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