NarGINA: Towards Accurate and Interpretable Children's Narrative Ability Assessment via Narrative Graphs

ACL ARR 2025 February Submission3843 Authors

Abstract: The assessment of children's narrative ability is crucial for diagnosing language disorders and planning interventions. Distinct from the typical automated essay scoring, this task focuses primarily on evaluating the completeness of narrative content and the coherence of expression, as well as the interpretability of assessment results. To address these issues, we propose a novel computational assessing framework NarGINA, under which the narrative graph is introduced to provide a concise and structured summary representation of narrative text, allowing for explicit narrative measurement. To this end, we construct the first Chinese children’s narrative assessment corpus based on real children’s narrative samples, and we then design a narrative graph construction model and a narrative graph-assisted scoring model to yield accurate narrative ability assessment. Particularly, to enable the scoring model to understand narrative graphs, we propose a multi-view graph contrastive learning strategy to pre-train the graph encoder and apply instruction-tuned large language models to generate scores. The extensive experimental results show that NarGINA can achieve significant performance improvement over the baselines, simultaneously possessing good interpretability. Our findings reveal that the utilization of structured narrative graphs beyond flat text is well suited for narrative ability assessment.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Narrative Ability Assessment, Automated Essay Scoring, Narrative Graph, Interpretability

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: Chinese

Submission Number: 3843