Data Augmentation for Cross-domain Parsing via Lightweight LLM Generation and Tree Hybridization
Ziyan Zhang, Yang Hou, Chen Gong, Zhenghua Li
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.
BibTeX
@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://aclanthology.org/2025.coling-main.744/",
pages = "11235--11247"
}