Conditional Hybrid GAN for Sequence Generation
  Conditional sequence generation aims to instruct the generation procedure by
conditioning the model with additional context information, which is a
self-supervised learning issue (a form of unsupervised learning with
supervision information from data itself). Unfortunately, the current
state-of-the-art generative models have limitations in sequence generation with
multiple attributes. In this paper, we propose a novel conditional hybrid GAN
(C-Hybrid-GAN) to solve this issue. Discrete sequence with triplet attributes
are separately generated when conditioned on the same context. Most
importantly, relational reasoning technique is exploited to model not only the
dependency inside each sequence of the attribute during the training of the
generator but also the consistency among the sequences of attributes during the
training of the discriminator. To avoid the non-differentiability problem in
GANs encountered during discrete data generation, we exploit the Gumbel-Softmax
technique to approximate the distribution of discrete-valued sequences.Through
evaluating the task of generating melody (associated with note, duration, and
rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms
the existing methods in context-conditioned discrete-valued sequence
generation.
