LLM-enhanced Self-training for Cross-domain Constituency Parsing
This work addresses cross-domain constituency parsing, an incremental improvement for NLP researchers and practitioners.
The paper tackles the problem of limited raw corpora in cross-domain constituency parsing by enhancing self-training with a large language model to generate domain-specific raw data iteratively, achieving performance improvements over traditional methods regardless of the LLM's performance.
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.