  Category text generation receives considerable attentions since it is
beneficial for various natural language processing tasks. Recently, the
generative adversarial network (GAN) has attained promising performance in text
generation, attributed to its adversarial training process. However, there are
several issues in text GANs, including discreteness, training instability, mode
collapse, lack of diversity and controllability etc. To address these issues,
this paper proposes a novel GAN framework, the feature-aware conditional GAN
(FA-GAN), for controllable category text generation. In FA-GAN, the generator
has a sequence-to-sequence structure for improving sentence diversity, which
consists of three encoders including a special feature-aware encoder and a
category-aware encoder, and one relational-memory-core-based decoder with the
Gumbel SoftMax activation function. The discriminator has an additional
category classification head. To generate sentences with specified categories,
the multi-class classification loss is supplemented in the adversarial
training. Comprehensive experiments have been conducted, and the results show
that FA-GAN consistently outperforms 10 state-of-the-art text generation
approaches on 6 text classification datasets. The case study demonstrates that
the synthetic sentences generated by FA-GAN can match the required categories
and are aware of the features of conditioned sentences, with good readability,
fluency, and text authenticity.
