@inproceedings{wang-etal-2025-minas,
title = "{MINAS}: {M}andarin Intelligent Narrative Assessment of Syntax for Children",
author = "Wang, Ruei-Ru and
Li, Ya-Sin and
Yin, Yi-Shuo and
Chen, Tao-Yu and
Cheung, Hint-Tat and
Chen, Ching-Tai",
editor = "Chang, Kai-Wei and
Lu, Ke-Han and
Yang, Chih-Kai and
Tam, Zhi-Rui and
Chang, Wen-Yu and
Wang, Chung-Che",
booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
month = nov,
year = "2025",
address = "National Taiwan University, Taipei City, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.rocling-main.22/",
pages = "184--192",
ISBN = "979-8-89176-379-1",
abstract = "Children{'}s narrative ability is an important indicator of language development and is commonly used in clinical diagnosis and linguistic research. However, the lack of large-scale, standardized, and accurately annotated Chinese child language corpora makes grammatical analysis both time-consuming and prone to subjectivity, while existing automated tools fall short of clinical and research needs. This study introduces MINAS (Mandarin Intelligent Narrative Assessment of Syntax for Children), which integrates the MAIN story framework with the MAPS-R syntactic framework to construct a Chinese narrative corpus encompassing four categories and 20 indicators. We evaluated commercial models (ChatGPT-4, Claude Sonnet 4, Gemini 2.5 Flash, DeepSeek) through prompt engineering, and fine-tuned open-source models (Chinese RoBERTa, OpenHermes-2.5) with LoRA. Experimental results show that few-shot prompting achieves high accuracy across most indicators, while fine-tuning with LoRA achieves better performance in noun and verb phrase identification but is not as good for complex sentence structures. This study validates the feasibility of applying large language models to syntactic classification of Chinese child narrative corpora, highlighting their potential in clinical applications and linguistic research."
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<abstract>Children’s narrative ability is an important indicator of language development and is commonly used in clinical diagnosis and linguistic research. However, the lack of large-scale, standardized, and accurately annotated Chinese child language corpora makes grammatical analysis both time-consuming and prone to subjectivity, while existing automated tools fall short of clinical and research needs. This study introduces MINAS (Mandarin Intelligent Narrative Assessment of Syntax for Children), which integrates the MAIN story framework with the MAPS-R syntactic framework to construct a Chinese narrative corpus encompassing four categories and 20 indicators. We evaluated commercial models (ChatGPT-4, Claude Sonnet 4, Gemini 2.5 Flash, DeepSeek) through prompt engineering, and fine-tuned open-source models (Chinese RoBERTa, OpenHermes-2.5) with LoRA. Experimental results show that few-shot prompting achieves high accuracy across most indicators, while fine-tuning with LoRA achieves better performance in noun and verb phrase identification but is not as good for complex sentence structures. This study validates the feasibility of applying large language models to syntactic classification of Chinese child narrative corpora, highlighting their potential in clinical applications and linguistic research.</abstract>
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%0 Conference Proceedings
%T MINAS: Mandarin Intelligent Narrative Assessment of Syntax for Children
%A Wang, Ruei-Ru
%A Li, Ya-Sin
%A Yin, Yi-Shuo
%A Chen, Tao-Yu
%A Cheung, Hint-Tat
%A Chen, Ching-Tai
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F wang-etal-2025-minas
%X Children’s narrative ability is an important indicator of language development and is commonly used in clinical diagnosis and linguistic research. However, the lack of large-scale, standardized, and accurately annotated Chinese child language corpora makes grammatical analysis both time-consuming and prone to subjectivity, while existing automated tools fall short of clinical and research needs. This study introduces MINAS (Mandarin Intelligent Narrative Assessment of Syntax for Children), which integrates the MAIN story framework with the MAPS-R syntactic framework to construct a Chinese narrative corpus encompassing four categories and 20 indicators. We evaluated commercial models (ChatGPT-4, Claude Sonnet 4, Gemini 2.5 Flash, DeepSeek) through prompt engineering, and fine-tuned open-source models (Chinese RoBERTa, OpenHermes-2.5) with LoRA. Experimental results show that few-shot prompting achieves high accuracy across most indicators, while fine-tuning with LoRA achieves better performance in noun and verb phrase identification but is not as good for complex sentence structures. This study validates the feasibility of applying large language models to syntactic classification of Chinese child narrative corpora, highlighting their potential in clinical applications and linguistic research.
%U https://aclanthology.org/2025.rocling-main.22/
%P 184-192
Markdown (Informal)
[MINAS: Mandarin Intelligent Narrative Assessment of Syntax for Children](https://aclanthology.org/2025.rocling-main.22/) (Wang et al., ROCLING 2025)
ACL
- Ruei-Ru Wang, Ya-Sin Li, Yi-Shuo Yin, Tao-Yu Chen, Hint-Tat Cheung, and Ching-Tai Chen. 2025. MINAS: Mandarin Intelligent Narrative Assessment of Syntax for Children. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 184–192, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.