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