Jianling Li


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LLM-enhanced Self-training for Cross-domain Constituency Parsing
Jianling Li | Meishan Zhang | Peiming Guo | Min Zhang | Yue Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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.


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Word Graph Guided Summarization for Radiology Findings
Jinpeng Hu | Jianling Li | Zhihong Chen | Yaling Shen | Yan Song | Xiang Wan | Tsung-Hui Chang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021