  Generative models reliant on sequential autoregression have been at the
forefront of language generation for an extensive period, particularly
following the introduction of widely acclaimed transformers. Despite its
excellent performance, there are always some issues that we face today. For
example, problems such as hallucinations and getting trapped in a logic loop
may occur. To enhance the performance of existing systems, this paper
introduces a new method for generating sequences in natural language, which
involves generating the targeted sentence in a tree-traversing order. The paper
includes an illustration of the theoretical basis and validity of the approach,
as well as a comparison of its fundamentals with the diffusion model in graphic
generation. Finally, a module called SenTree is introduced for generating an
approximating binary tree. It is already available at
https://github.com/arklyg/sentree. Additionally, a joint training framework
based on this approach is proposed, incorporating the intrinsics of generative
adversarial networks.
