Personalized sentence generation using generative adversarial networks with author-specific word usage
This work addresses the problem of generating stylistically consistent text for specific authors, though it is incremental as it builds on existing GAN methods.
The paper tackled personalized sentence generation by incorporating author-specific word usage and named entity recognition into a GAN framework, resulting in generated sentences that were more similar to the original author's style, as shown by improved BLEU and SimHash scores.
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.