TicketTalk
Introduced by Byrne et al. in TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems
A movie ticketing dialog dataset with 23,789 annotated conversations. The movie ticketing conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to "make sense" 86.5 percent of the time, almost the same as human responses in the same contexts.
Source: TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systemsPapers
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