Standard sequential generation methods assume a pre-specified generation
order, such as text generation methods which generate words from left to right.
In this work, we propose a framework for training models of text generation
that operate in non-monotonic orders; the model directly learns good orders,
without any additional annotation. Our framework operates by generating a word
at an arbitrary position, and then recursively generating words to its left and
then words to its right, yielding a binary tree. Learning is framed as
imitation learning, including a coaching method which moves from imitating an
oracle to reinforcing the policy's own preferences. Experimental results
demonstrate that using the proposed method, it is possible to learn policies
which generate text without pre-specifying a generation order, while achieving
competitive performance with conventional left-to-right generation.

本文使用递归二叉树模型，提出了一种用于生成文本的框架，能够训练模型采用非单调生成方式，模型能够直接学习适宜的生成次序，且不需要额外的标注。文章利用模拟学习的方法，从模仿 Oracle 到加强自己的喜好，达到了无需预定生成顺序的文字生成，同时达到了与传统的从左到右生成相当的性能。