Towards Better Variational Encoder-Decoders in Seq2Seq Tasks


  • Xiaoyu Shen Max Planck Institute Informatics
  • Hui Su Software Institute, University of Chinese Academy of Science, China


Variational encoder-decoders have shown promising results in seq2seq tasks. However, the training process is known difficult to be controlled because latent variables tend to be ignored while decoding. In this paper, we thoroughly analyze the reason behind this training difficulty, compare different ways of alleviating it and propose a new framework that helps significantly improve the overall performance.




How to Cite

Shen, X., & Su, H. (2018). Towards Better Variational Encoder-Decoders in Seq2Seq Tasks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from