@inproceedings{sun-etal-2026-capc,
title = "{CAPC}-{CG}: A Large-Scale, Expert-Directed {LLM}-Annotated Corpus of Adaptive Policy Communication in {C}hina",
author = "Sun, Bolun and
Chang, Charles and
Ang, Yuen Yuen and
Mu, Ruotong and
Xu, Yuchen and
Zhang, Zhengxin and
Hao, Pingxu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.42/",
doi = "10.18653/v1/2026.acl-long.42",
pages = "944--966",
ISBN = "979-8-89176-390-6",
abstract = "We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives annotated with a five-color typology of policy signals, capturing clarity and ambiguity, grounded in the theory of adaptive policy communication. Spanning 1949{--}2023, this corpus includes laws, regulations, and rules issued by Chinese central authorities, segmented into 3.3 million paragraph units. We further propose and validate an expert-directed LLM annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling. Alongside the corpus, we release metadata and a gold-standard labeled set developed by trained coders. Inter-annotator agreement achieves a Fleiss' kappa of {\ensuremath{\kappa}} = 0.86 on directive labels, indicating high reliability. We provide baseline classification results with several large language models (LLMs), together with our codebook, and describe patterns from the data. This release enables downstream tasks and multilingual NLP research in communication strategies under complexity and uncertainty."
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%0 Conference Proceedings
%T CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China
%A Sun, Bolun
%A Chang, Charles
%A Ang, Yuen Yuen
%A Mu, Ruotong
%A Xu, Yuchen
%A Zhang, Zhengxin
%A Hao, Pingxu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F sun-etal-2026-capc
%X We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives annotated with a five-color typology of policy signals, capturing clarity and ambiguity, grounded in the theory of adaptive policy communication. Spanning 1949–2023, this corpus includes laws, regulations, and rules issued by Chinese central authorities, segmented into 3.3 million paragraph units. We further propose and validate an expert-directed LLM annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling. Alongside the corpus, we release metadata and a gold-standard labeled set developed by trained coders. Inter-annotator agreement achieves a Fleiss’ kappa of \ensuremathąppa = 0.86 on directive labels, indicating high reliability. We provide baseline classification results with several large language models (LLMs), together with our codebook, and describe patterns from the data. This release enables downstream tasks and multilingual NLP research in communication strategies under complexity and uncertainty.
%R 10.18653/v1/2026.acl-long.42
%U https://aclanthology.org/2026.acl-long.42/
%U https://doi.org/10.18653/v1/2026.acl-long.42
%P 944-966
Markdown (Informal)
[CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China](https://aclanthology.org/2026.acl-long.42/) (Sun et al., ACL 2026)
ACL
- Bolun Sun, Charles Chang, Yuen Yuen Ang, Ruotong Mu, Yuchen Xu, Zhengxin Zhang, and Pingxu Hao. 2026. CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 944–966, San Diego, California, United States. Association for Computational Linguistics.