Most existing text generation models follow the sequence-to-sequence
paradigm. Generative Grammar suggests that humans generate natural language
texts by learning language grammar. We propose a syntax-guided generation
schema, which generates the sequence guided by a constituency parse tree in a
top-down direction. The decoding process can be decomposed into two parts: (1)
predicting the infilling texts for each constituent in the lexicalized syntax
context given the source sentence; (2) mapping and expanding each constituent
to construct the next-level syntax context. Accordingly, we propose a
structural beam search method to find possible syntax structures
hierarchically. Experiments on paraphrase generation and machine translation
show that the proposed method outperforms autoregressive baselines, while also
demonstrating effectiveness in terms of interpretability, controllability, and
diversity.

我们提出了一种基于句法树的语法引导生成模式，通过解码过程的两个部分:（1）对给定源句子中词汇化的句法上下文中的每个成分预测填充文本；（2）映射和扩展每个成分以构建下一级语法上下文以生成自然语言文本，并在重述生成和机器翻译上进行了实验。与自回归基线相比，该方法在有效性和可解释性方面更加出色。