  Diversity plays a vital role in many text generating applications. In recent
years, Conditional Variational Auto Encoders (CVAE) have shown promising
performances for this task. However, they often encounter the so called
KL-Vanishing problem. Previous works mitigated such problem by heuristic
methods such as strengthening the encoder or weakening the decoder while
optimizing the CVAE objective function. Nevertheless, the optimizing direction
of these methods are implicit and it is hard to find an appropriate degree to
which these methods should be applied. In this paper, we propose an explicit
optimizing objective to complement the CVAE to directly pull away from
KL-vanishing. In fact, this objective term guides the encoder towards the "best
encoder" of the decoder to enhance the expressiveness. A labeling network is
introduced to estimate the "best encoder". It provides a continuous label in
the latent space of CVAE to help build a close connection between latent
variables and targets. The whole proposed method is named Self Labeling
CVAE~(SLCVAE). To accelerate the research of diverse text generation, we also
propose a large native one-to-many dataset. Extensive experiments are conducted
on two tasks, which show that our method largely improves the generating
diversity while achieving comparable accuracy compared with state-of-art
algorithms.
