  The author-specific word usage is a vital feature to let readers perceive the
writing style of the author. In this work, a personalized sentence generation
method based on generative adversarial networks (GANs) is proposed to cope with
this issue. The frequently used function word and content word are incorporated
not only as the input features but also as the sentence structure constraint
for the GAN training. For the sentence generation with the related topics
decided by the user, the Named Entity Recognition (NER) information of the
input words is also used in the network training. We compared the proposed
method with the GAN-based sentence generation methods, and the experimental
results showed that the generated sentences using our method are more similar
to the original sentences of the same author based on the objective evaluation
such as BLEU and SimHash score.
