  Text generation is a fundamental building block in natural language
processing tasks. Existing sequential models performs autoregression directly
over the text sequence and have difficulty generating long sentences of complex
structures. This paper advocates a simple approach that treats sentence
generation as a tree-generation task. By explicitly modelling syntactic
structures in a constituent syntactic tree and performing top-down,
breadth-first tree generation, our model fixes dependencies appropriately and
performs implicit global planning. This is in contrast to transition-based
depth-first generation process, which has difficulty dealing with incomplete
texts when parsing and also does not incorporate future contexts in planning.
Our preliminary results on two generation tasks and one parsing task
demonstrate that this is an effective strategy.
