which connects the decoder with latent variable by a novel explicit optimization objective. With this objective, the encoder is pulled towards the “best encoder" defined by current decoder and the prior distribution simultaneously, which leads to equilibrium at which the encoder distribution is close to the prior and also remains the expressiveness. Thus the KL-vanishing problem can be significantly relieved. Further, extensive experiments demonstrates that our method called SLCVAE owns better ability to model multiple targets and improves the diversity of text generation without losing accuracy. Thirdly, a large scale dataset called EGOODS which contains native one-to-many text data of high quality is constructed to accelerate the research of diverse text generation.

![image](sl_cvae){width="0.95\linewidth"}

\[fig:slcvae\]

Related Work
============

In this section, we review the development of both the encoder-decoder models and VAE/CVAE based models for text generation.

Encoder-decoder model
---------------------

Encoder-decoder models are commonly adopted in NLP as they are able to fit complex data by end-to-end training. The presentation of SEQ2SEQ[@sutskever2014sequence] structure revolutionarily augmented the quality of Machine Translation (MT). And researchers soon introduce such structure into text generating systems[@serban2016building; @vinyals2015neural]. However, the purpose of the SEQ2SEQ models is to best fit the target sequence given the source sequence. Therefore 