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The goal of building intelligent dialogue systems has largely been separatelypursued under two motives: task-oriented dialogue (TOD) systems, andopen-domain systems for chit-chat (CC). Although previous TOD dialogue systemswork well in the testing sets of benchmarks, they would lead to undesirablefailure when being exposed to natural scenarios in practice, where userutterances can be of high motive-diversity that fusing both TOD and CC inmulti-turn interaction. Since an industrial TOD system should be able toconverse with the user between TOD and CC motives, constructing a fuse-motivedialogue dataset that contains both TOD or CC is important. Most prior workrelies on crowd workers to collect and annotate large scale dataset and isrestricted to English language setting. Our work, on the contrary, addressesthis problem in a more effective way and releases a multi-turn dialoguesdataset called CCET (Chinese Chat-Enhanced-Task). Meanwhile, we also propose aline of fuse-motive dialogues formalization approach, along with severalevaluation metrics for TOD sessions that are integrated by CC utterances.

A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems