@inproceedings{zhang-etal-2025-data,
    title = "Data Augmentation for Cross-domain Parsing via Lightweight {LLM} Generation and Tree Hybridization",
    author = "Zhang, Ziyan  and
      Hou, Yang  and
      Gong, Chen  and
      Li, Zhenghua",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.744/",
    pages = "11235--11247",
    abstract = "Cross-domain constituency parsing remains a challenging task due to the lack of high-quality out-of-domain data. In this paper, we propose a data augmentation method via lightweight large language model (LLM) generation and tree hybridization. We utilize LLM to generate phrase structures (subtrees) for the target domain by incorporating grammar rules and lexical head information into the prompt. To better leverage LLM-generated target-domain subtrees, we hybridize them with existing source-domain subtrees to efficiently produce a large number of structurally diverse instances. Experimental results demonstrate that our method achieves significant improvements on five target domains with a lightweight LLM generation cost."
}