N ar GINA : Towards Accurate and Interpretable Children’s Narrative Ability Assessment via Narrative Graphs (2025)

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. The model and data are publicly available at https://github.com/JlexZzz/NarGINA.

## introduction a narrative can take several forms: recounting past experiences, retelling a previously heard or read story, or creating a composition @xcite . assessing narrative ability not only provides an objective measure of children's language development, but also plays a crucial role in the early diagnosis and intervention of language disorders @xcite . in the field of clinical linguistics, assessing narrative ability has been a major focus of research. studies typically analyze narratives from two perspectives: macrostructure @xcite and microstructure @xcite . macrostructure refers to the global organization of a story, typically defined by story grammar components or story structure @xcite . in contrast, microstructure focuses on local linguistic features, including story length, lexical diversity, syntactic complexity, and cohesion. as microstructural features are relatively easy to quantify, research has increasingly emphasized macrostructural coherence @xcite and completeness @xcite . causal networks @xcite are an important tool for assessing these aspects @xcite , providing an intuitive representation of narrative macrostructure. however, researchers in this field generally rely purely on manual analyses of children's narrative samples, which poses a practical dilemma of being time-consuming and laborious; therefore, it is difficult to promote and apply in broader practices. this paper focuses on automated assessment of children's narrative ability by exploring the forefront natural language processing (nlp) tech-niques. outwardly, this task shares similarities with the multi-trait automated essay scoring (aes), which evaluates various essay genres across traits like content and language use. some recent studies have applied autoregressive multi-trait score generation framework to leverage token generation probabilities @xcite . nevertheless, compared to multi-trait aes, the automated assessment of children's narrative ability presents unique challenges in the following aspects: (1) the narrative assessment task focuses primarily on evaluating the completeness of narrative content and the coherence of expression; (2) the assessment result of this task requires not only high accuracy, as well as the intuitiveness and interpretability, which are essential to provide actionable feedback for subsequent interventions. there has also been some sporadic research on this task. @xcite employed topic modeling to predict language disorders and coherence. @xcite simply used machine learning methods to score macrostructure. obviously, these works have not presented effective solutions to the aforementioned challenges. to address these issues, we propose the narrative graph-based interpretable children's narrative ability assessment (nargina) framework. to this end, we first introduce a narrative graph as a structured representation of narrative text, inspired by the causal networks in clinical linguistics @xcite . though the causal network gives an intuitive representation of the input text, that structure simply considers clauses as nodes, which makes it difficult to clearly express the complex narrative content. contrastively, in our narrative graph, nodes represent specific events, and edges capture event relations, such as various causal and synchronous connections (see an example in figure 1 ). compared to flat and unstructured narrative text, the narrative graph provides a concise summary representation, thus helping to explicitly measure and calculate the key narrative indicators such as completeness and coherence; meanwhile, the interpretability can also be naturally facilitated through the comparative analysis between the evaluation results and the goldstandard narrative graph 1 . further, we design the computational framework nargina, based on the narrative graph, for assessing narrative ability. unlike most existing aes systems that rely solely on feature learning from in a nutshell, our contributions are as follows: ## corpus construction the chinese children's narrative assessment corpus comprises 543 annotated narrative texts, each paired with a narrative graph and scores for overall ability and three key traits: macrostructure, microstructure, and psychological states. ## conclusion in this paper, we focus on the automated assessment of children's narrative ability. we propose a novel computational assessing framework nargina that introduces the narrative graph to explicitly measure and calculate the key narrative indicators such as completeness and coherence. we construct the first chinese children's narrative assessment corpus, and then propose the narrative graph construction model and a narrative graph-assisted scoring model. experimental results demonstrate that nargina substantially outperforms the baselines, along with good interpretability. in particular, our findings reveal that the utilization of structured narrative graphs beyond flat text is well suited for narrative ability assessment. in future work, we will explore more effective narrative graph construction and scoring models to achieve better performance.