accuracy compared with state-of-art algorithms.'
author:
- |
    Yuchi Zhang [^1], Yongliang Wang, Liping Zhang, Zhiqiang Zhang, Kun Gai\
    Alibaba Group\
    Beijing, China, 100102\
bibliography:
- 'Bibliography-File.bib'
title: |
    Improve Diverse Text Generation by Self Labeling\
    Conditional Variational Auto Encoder
---

Introduction
============

Text generating techniques are widely used in various tasks, such as dialogue generation [@serban2016building; @li2016deep; @zhao2017learning], image caption [@chen2015mind; @vinyals2015show; @xu2015show] and question-answer systems [@beamon2017justifying; @oh2017non], etc. Recently, encoder-decoder models such as SEQ2SEQ[@sutskever2014sequence] have been increasingly adopted in text generating tasks. Encoder-decoder models extract a semantic representation from the input and generate sentences coherent to the input according to this representation. They perform well in tasks which require accuracy and relativeness. However, applications such as dialogue systems further require results with diversity besides accuracy. Conventional encoder-decoder models are not good at handling such situations due to its deterministic nature. In open domain conversation systems, given the dialogue history, there may exist various kinds of responses which are grammatically correct and semantically meaningful. The dialogue bots should be able to model these multiple responses for the same input in training and give diverse answers like humans in predicting. Another application takes place in e-commerce recommendation systems. For a 