  Many classical fairy tales, fiction, and screenplays leverage dialogue to
advance story plots and establish characters. We present the first study to
explore whether machines can understand and generate dialogue in stories, which
requires capturing traits of different characters and the relationships between
them. To this end, we propose two new tasks including Masked Dialogue
Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue
turns and predicting speakers for specified dialogue turns, respectively. We
build a new dataset DialStory, which consists of 105k Chinese stories with a
large amount of dialogue weaved into the plots to support the evaluation. We
show the difficulty of the proposed tasks by testing existing models with
automatic and manual evaluation on DialStory. Furthermore, we propose to learn
explicit character representations to improve performance on these tasks.
Extensive experiments and case studies show that our approach can generate more
coherent and informative dialogue, and achieve higher speaker recognition
accuracy than strong baselines.
