Self-training has proven to be an effective approach for cross-domain tasks,
and in this study, we explore its application to cross-domain constituency
parsing. Traditional self-training methods rely on limited and potentially
low-quality raw corpora. To overcome this limitation, we propose enhancing
self-training with the large language model (LLM) to generate domain-specific
raw corpora iteratively. For the constituency parsing, we introduce grammar
rules that guide the LLM in generating raw corpora and establish criteria for
selecting pseudo instances. Our experimental results demonstrate that
self-training for constituency parsing, equipped with an LLM, outperforms
traditional methods regardless of the LLM's performance. Moreover, the
combination of grammar rules and confidence criteria for pseudo-data selection
yields the highest performance in the cross-domain constituency parsing.

通过结合大型语言模型（LLM）生成领域特定原始语料库的迭代过程，以及引入语法规则指导 LLM 生成原始语料库并建立伪实例选择标准等方法，本研究探索了自训练方法在跨领域句法分析中的应用，实验证明，自训练方法配备 LLM 在句法分析任务上胜过传统方法，且通过语法规则和置信度标准选择伪数据的组合效果最佳。