Joel Mire, Maria Antoniak, Steven R Wilson, Zexin Ma, Achyutarama R Ganti, Andrew Piper, Maarten Sap. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Narrative flow emerges from the interplay between memory and expectation, shaping how stories are both produced and understood.To operationalize this construct, Sap et al. (2022) propose sequentiality, a language-model-based measure of sentence-level predictability, and report that imagined stories flow better than recalled ones.We conduct a large-scale replication across multiple language models, examine how modeling choices shape the original findings, and test generalization beyond crowdworker data using passages from published fiction and narrative non-fiction.Although the original contrast replicates under their initial formulation, it diminishes substantially under alternative specifications, suggesting that it reflects properties of the measurement setup rather than a stable feature of narrative flow.By contrast, fiction does appear to exhibit a robust sequentiality advantage over reality-bound genres under a minimal contextonly formulation.However, mixed-effects analyses indicate that this advantage is not reducible to standard coherence measures, underscoring the need for further theoretical and empirical grounding of narrative flow.

Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

We introduce Taxonomy-Free Character Role Labeling (TF-CRL); a novel task that assigns open-ended narrative role labels to characters in news stories based on their functional role in the narrative.Unlike fixed taxonomies, TF-CRL enables more nuanced and comparative analysis by generating compositional labels (e.g., Resilient Leader, Scapegoated Visionary).We evaluate several large language models (LLMs) on this task using human preference rankings and ratings across four criteria: faithfulness, relevance, informativeness, and generalizability.LLMs almost uniformly outperform human annotators across all dimensions.We further show how TF-CRL supports rich narrative analysis by revealing novel latent taxonomies and enabling cross-domain narrative comparisons.Our approach offers new tools for studying media portrayals, character framing, and the socio-political impacts of narrative roles at-scale.1

Moral Foundations Theory (MFT) provides a framework for categorizing different forms of moral reasoning, but its application to computational narrative analysis remains limited.We propose a novel character-centric method to quantify moral foundations in storytelling, using large language models (LLMs) and a novel Moral Foundations Character Action Questionnaire (MFCAQ) to evaluate the moral foundations supported by the behaviour of characters in stories.We validate our approach against human annotations and then apply it to a study of 2,697 folktales from 55 countries.Our findings reveal: (1) broad distribution of moral foundations across cultures, (2) significant crosscultural consistency with some key regional differences, and (3) a more balanced distribution of positive and negative moral content than suggested by prior work.This work connects MFT and computational narrative analysis, demonstrating LLMs' potential for scalable moral reasoning in narratives. 1

We introduce "Citizen Readers for Narrative Emotions" (CR4-NarrEmote), a large-scale, open-vocabulary dataset of narrative emotions derived through a citizen science initiative.Over a four-month period, 3,738 volunteers contributed more than 200,000 emotion annotations across 43,000 passages from long-form fiction and non-fiction, spanning 150 years, twelve genres, and multiple Anglophone cultural contexts.To facilitate model training and comparability, we provide mappings to both dimensional (Valence-Arousal-Dominance) and categorical (NRC Emotion) frameworks.We evaluate annotation reliability using lexical, categorical, and semantic agreement measures, and find substantial alignment between citizen science annotations and expert-generated labels.As the first open-vocabulary resource focused on narrative emotions at scale, CR4-NarrEmote provides an important foundation for affective computing and narrative understanding.

Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text.We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels.Our dataset is sampled from hundreds of popular Englishlanguage Reddit communities ranging across 33 topic categories, and it contains fine-grained expert annotations, including binary story labels, story spans, and event spans.We evaluate a range of detection methods using our data, and we identify the distinctive textual features of online storytelling, focusing on storytelling spans.We illuminate distributional characteristics of storytelling on a large communitycentric social media platform, and we also conduct a case study on r/ChangeMyView, where storytelling is used as one of many persuasive strategies, illustrating that our data and models can be used for both inter-and intra-community research.Finally, we discuss implications of our tools and analyses for narratology and the study of online communities.

Stories are not only designed to entertain but to encode lessons reflecting their authors' beliefs about the world.In this paper, we propose a new task of narrative schema labelling based on the concept of "story morals" to identify the values and lessons conveyed in stories.Using large language models (LLMs) such as GPT-4, we develop methods to automatically extract and validate story morals across a diverse set of narrative genres, including folktales, novels, movies and TV, personal stories from social media, and the news.Our approach involves a multi-step prompting sequence to derive morals and validate them through both automated metrics and human assessments.The findings suggest that LLMs can effectively approximate human story moral interpretations and offer a new avenue for computational narrative understanding.By clustering the extracted morals on a sample dataset of folktales from around the world, we highlight the commonalities and distinctiveness of narrative values, providing preliminary insights into the distribution of values across cultures.This work opens up new possibilities for studying narrative schemas and their role in shaping human beliefs and behaviors.1

Most NLP work on narrative detection has focused on prescriptive definitions of stories crafted by researchers, leaving open the questions: how do crowd workers perceive texts to be a story, and why?We investigate this by building STORYPERCEPTIONS, a dataset of 2,496 perceptions of storytelling in 502 social media texts from 255 crowd workers, including categorical labels along with free-text storytelling rationales, authorial intent, and more.We construct a fine-grained bottom-up taxonomy of crowd workers' varied and nuanced perceptions of storytelling by open-coding their free-text rationales.Through comparative analyses at the label and code level, we illuminate patterns of disagreement among crowd workers and across other annotation contexts, including prescriptive labeling from researchers and LLM-based predictions.Notably, plot complexity, references to generalized or abstract actions, and holistic aesthetic judgments (such as a sense of cohesion) are especially important in disagreements.Our empirical findings broaden understanding of the types, relative importance, and contentiousness of features relevant to narrative detection, highlighting opportunities for future work on reader-contextualized models of narrative reception.

Abusive language in online discourse negatively affects a large number of social media users.Many computational methods have been proposed to address this issue of online abuse.The existing work, however, tends to focus on detecting the more explicit forms of abuse leaving the subtler forms of abuse largely untouched.Our work addresses this gap by making three core contributions.First, inspired by the theory of impoliteness, we propose a novel task of detecting a subtler form of abuse, namely unpalatable questions.Second, we publish a context-aware dataset for the task using data from a diverse set of Reddit communities.Third, we implement a wide array of learning models and also investigate the benefits of incorporating conversational context into computational models.Our results show that modeling subtle abuse is feasible but difficult due to the language involved being highly nuanced and context-sensitive.We hope that future research in the field will address such subtle forms of abuse since their harm currently passes unnoticed through existing detection systems.

Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection.While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences.In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.

Anaphor resolution is an important task in NLP with many applications.Despite much research effort, it remains an open problem.The difficulty of the problem varies substantially across different sub-problems.One sub-problem, in particular, has been largely untouched by prior work despite occurring frequently throughout corpora: the anaphor that has multiple antecedents, which here we call multi-antecedent anaphors or manaphors.Current coreference resolvers restrict anaphors to at most a single antecedent.As we show in this paper, relaxing this constraint poses serious problems in coreference chain-building, where each chain is intended to refer to a single entity.This work provides a formalization of the new task with preliminary insights into multi-antecedent noun-phrase anaphors, and offers a method for resolving such cases that outperforms a number of baseline methods by a significant margin.Our system uses local agglomerative clustering on candidate antecedents and an existing coreference system to score clusters to determine which cluster of mentions is antecedent for a given anaphor.When we augment an existing coreference system with our proposed method, we observe a substantial increase in performance (0.6 absolute CoNLL F1) on an annotated corpus.

Characters are fundamental to literary analysis.Current approaches are heavily reliant on NER to identify characters, causing many to be overlooked.We propose a novel technique for character detection, achieving significant improvements over state of the art on multiple datasets.