We present a hierarchical knowledge graph framework for the structured semantic understanding of visual narratives, using comics as a representative domain for multimodal storytelling. The framework organizes narrative content across three levels-panel, event, and macro-event, by integrating symbolic graphs that encode semantic, spatial, and temporal relationships. At the panel level, it models visual elements such as characters, objects, and actions alongside textual components including dialogue and narration. These are systematically connected to higher-level graphs that capture narrative sequences and abstract story structures. Applied to a manually annotated subset of the Manga109 dataset, the framework supports interpretable symbolic reasoning across four representative tasks: action retrieval, dialogue tracing, character appearance mapping, and timeline reconstruction. Rather than prioritizing predictive performance, the system emphasizes transparency in narrative modeling and enables structured inference aligned with cognitive theories of event segmentation and visual storytelling. This work contributes to explainable narrative analysis and offers a foundation for authoring tools, narrative comprehension systems, and interactive media applications.

Understanding visual narratives such as comics requires structured representations that capture events, characters, and their relations across multiple levels of story organization. However, symbolic narrative graphs often suffer from inconsistency and redundancy, where similar actions or events are labeled differently across annotations or contexts. Such variance limits the effectiveness of reasoning and generalization. This paper introduces a semantic normalization framework for hierarchical narrative knowledge graphs. Building on cognitively grounded models of narrative comprehension, we propose methods that consolidate semantically related actions and events using lexical similarity and embedding-based clustering. The normalization process reduces annotation noise, aligns symbolic categories across narrative levels, and preserves interpretability. We demonstrate the framework on annotated manga stories from the Manga109 dataset, applying normalization to panel-, event-, and story-level graphs. Preliminary evaluations across narrative reasoning tasks, such as action retrieval, character grounding, and event summarization, show that semantic normalization improves coherence and robustness, while maintaining symbolic transparency. These findings suggest that normalization is a key step toward scalable, cognitively inspired graph models for multimodal narrative understanding.

Question Answering (QA) datasets are crucial in assessing reading comprehension skills for both machines and humans. While numerous datasets have been developed in English for this purpose, a noticeable void exists in less-resourced languages. To alleviate this gap, our paper introduces machine-translated versions of FairytaleQA, a renowned QA dataset designed to assess and enhance narrative comprehension skills in young children. By employing fine-tuned, modest-scale models, we establish benchmarks for both Question Generation (QG) and QA tasks within the translated datasets. In addition, we present a case study proposing a model for generating question-answer pairs, with an evaluation incorporating quality metrics such as question well-formedness, answerability, relevance, and children suitability. Our evaluation prioritizes quantifying and describing error cases, along with providing directions for future work. This paper contributes to the advancement of QA and QG research in less-resourced languages, promoting accessibility and inclusivity in the development of these models for reading comprehension. The code and data is publicly available at github.com/bernardoleite/fairytaleqa-translated.

Question Generation aims to automatically generate questions based on a given input provided as context. A controllable question generation scheme focuses on generating questions with specific attributes, allowing better control. In this study, we propose a few-shot prompting strategy for controlling the generation of question-answer pairs from children's narrative texts. We aim to control two attributes: the question's explicitness and underlying narrative elements. With empirical evaluation, we show the effectiveness of controlling the generation process by employing few-shot prompting side by side with a reference model. Our experiments highlight instances where the few-shot strategy surpasses the reference model, particularly in scenarios such as semantic closeness evaluation and the diversity and coherency of question-answer pairs. However, these improvements are not always statistically significant. The code is publicly available at github.com/bernardoleite/few-shot-prompting-qg-control.

This paper introduces a non-native speech corpus consisting of narratives from fifty 5- to 6-year-old Chinese-English children. Transcripts totaling 6.5 hours of children taking a narrative comprehension test in English (L2) are presented, along with human-rated scores and annotations of grammatical and pronunciation errors. The children also completed the parallel MAIN tests in Chinese (L1) for reference purposes. For all tests we recorded audio and video with our innovative self-developed remote collection methods. The video recordings serve to mitigate the challenge of low intelligibility in L2 narratives produced by young children during the transcription process. This corpus offers valuable resources for second language teaching and has the potential to enhance the overall performance of automatic speech recognition (ASR).

We present work toward computationally defining a model of narrative comprehension vis-à-vis memory of narrative events, via an automated planning knowledge representation, capable of being used in a narrative generation context.

This book describes a theory of memory representation, organization, and processing for understanding complex narrative texts. The theory is implemented as a computer program called BORIS which reads and answers questions about divorce, legal disputes, personal favors, and the like. The system is unique in attempting to understand stories involving emotions and in being able to deduce adages and morals, in addition to answering fact and event based questions about the narratives it has read. BORIS also manages the interaction of many different knowledge sources such as goals, plans, scripts, physical objects, settings, interpersonal relationships, social roles, emotional reactions, and empathetic responses. The book makes several original technical contributions as well.