  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.
