  In the past few years, audiences from different fields witness the
achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer
Generator Networks, and Transformer) to enhance dialogue content generation.
While content fluency and accuracy often serve as the major indicators for
model training, dialogue logics, carrying critical information for some
particular domains, are often ignored. Take customer service and court debate
dialogue as examples, compatible logics can be observed across different
dialogue instances, and this information can provide vital evidence for
utterance generation. In this paper, we propose a novel network architecture -
Cross Copy Networks(CCN) to explore the current dialog context and similar
dialogue instances' logical structure simultaneously. Experiments with two
tasks, court debate and customer service content generation, proved that the
proposed algorithm is superior to existing state-of-art content generation
models.
