AI Stories is a proposed interactive dialogue system, that lets children co-create narrative worlds through conversation. Over the next three years this system will be developed and tested within pediatric wards, where it offers a useful resource between the gap of education and play. Telling and making stories is a fundamental part of language play, and its chatty and nonsensical qualities are important; therefore, the prologued usage an automated system offers is a benefit to children. In this paper I will present the current state of this project, in its more experimental and general guise. Conceptually story-telling through dialogue relates to the preprint interpretation of story, beyond the static and linear medium, where stories were performative, temporal, and social.
University of Washington（华盛顿大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Microsoft（微软）
Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story. However, comparable computational tools to evaluate the flow of events in narratives are limited. We quantify the differences between autobiographical and imagined stories by introducing sequentiality, a measure of narrative flow of events, drawing probabilistic inferences from a cutting-edge large language model (GPT-3). Sequentiality captures the flow of a narrative by comparing the probability of a…
Ss. Cyril and Methodius University（圣基里尔与美多德大学）；Pendulibrium；Elevate Global
Recent research in AI is focusing towards generating narrative stories about visual scenes. It has the potential to achieve more human-like understanding than just basic description generation of images- in-sequence. In this work, we propose a solution for generating stories for images-in-sequence that is based on the Sequence to Sequence model. As a novelty, our encoder model is composed of two separate encoders, one that models the behaviour of the image sequence and other that models the sentence-story generated for the previous image in the sequence of images. By using the image sequence e…
We propose a method for controlled narrative/story generation where we are able to guide the model to produce coherent narratives with user-specified target endings by interpolation: for example, we are told that Jim went hiking and at the end Jim needed to be rescued, and we want the model to incrementally generate steps along the way. The core of our method is an interpolation model based on GPT-2 which conditions on a previous sentence and a next sentence in a narrative and fills in the gap. Additionally, a reranker helps control for coherence of the generated text. With human evaluation, w…
The Power of Stories: Narrative Priming Shapes How LLM Agents Collaborate and Compete
Department of Data Science and its Applications, German Research Center for Artificial Intelligence (DFKI)（数据科学及其应用系，德国人工智能研究中心（DFKI））；Department of Computer Science, University of Kaiserslautern–Landau (RPTU)（计算机科学系，凯撒斯劳滕-兰道大学（RPTU））；Department of Language Science and Technology, Saarland University（语言科学与技术系，萨尔兰大学）
本文通过有限重复公共物品博弈实验发现，基于故事的叙事启动能显著影响 LLM 智能体的合作与竞争策略，共同叙事促进合作，而不同叙事使自利倾向智能体占优，对多智能体系统设计和 AI 对齐具有启示。
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Many implicit inferences exist in text depending on how it is structured that can critically impact the text's interpretation and meaning. One such structural aspect present in text with chronology is the order of its presentation. For narratives or stories, this is known as the narrative order. Reordering a narrative can impact the temporal, causal, event-based, and other inferences readers draw from it, which in turn can have strong effects both on its interpretation and interestingness. In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewrit…
BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories
School of Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院，中国沈阳）；NiuTrans Research, Shenyang, China（NiuTrans研究院，中国沈阳）
Sirius-Beta；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Carnegie Mellon University（卡内基梅隆大学）
Stories are a vital form of communication in human culture; they are employed daily to persuade, to elicit sympathy, or to convey a message. Computational understanding of human narratives, especially high-level narrative structures, remain limited to date. Multiple literary theories for narrative structures exist, but operationalization of the theories has remained a challenge. We developed an annotation scheme by consolidating and extending existing narratological theories, including Labov and Waletsky's (1967) functional categorization scheme and Freytag's (1863) pyramid of dramatic tension…
University of Southern California（南加州大学）；Tokyo University（东京大学）；University of California, Los Angeles（加州大学洛杉矶分校）
Stories or narratives are comprised of a sequence of events. To compose interesting stories, professional writers often leverage a creative writing technique called flashback that inserts past events into current storylines as we commonly observe in novels and plays. However, it is challenging for machines to generate flashback as it requires a solid understanding of event temporal order (e.g. "feeling hungry" before "eat," not vice versa), and the creativity to arrange storylines so that earlier events do not always appear first in narrative order. Two major issues in existing systems that ex…
Dalle Molle Institute for Artificial Intelligence Research（达勒·莫勒人工智能研究所）；Università della Svizzera italiana（瑞士意大利语区大学）；Scuola universitaria professionale della Svizzera italiana（瑞士意大利语区应用科学大学）；University of Alberta（阿尔伯塔大学）；NNAISENSE；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define to be the overlap in information space between a story and the items that compose the story. Using contrastive learning methods, we show how modern artificial neural networks can be leveraged to distill stories and extract a representation of the narrative information. We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative templates and how these templates -- in tandem with a novel curve-fitting algori…
We present a new corpus, PersonaBank, consisting of 108 personal stories from weblogs that have been annotated with their Story Intention Graphs, a deep representation of the fabula of a story. We describe the topics of the stories and the basis of the Story Intention Graph representation, as well as the process of annotating the stories to produce the Story Intention Graphs and the challenges of adapting the tool to this new personal narrative domain We also discuss how the corpus can be used in applications that retell the story using different styles of tellings, co-tellings, or as a conten…
Yandex（扬德克斯）；ICT RAS（俄罗斯科学院信息传输问题研究所）；LEYA Laboratory（LEYA实验室）；Higher School of Economincs（高等经济大学）
This paper presents StoryDB - a broad multi-language dataset of narratives. StoryDB is a corpus of texts that includes stories in 42 different languages. Every language includes 500+ stories. Some of the languages include more than 20 000 stories. Every story is indexed across languages and labeled with tags such as a genre or a topic. The corpus shows rich topical and language variation and can serve as a resource for the study of the role of narrative in natural language processing across various languages including low resource ones. We also demonstrate how the dataset could be used to benc…
Princeton University（普林斯顿大学）；New Jersey Institute of Technology（新泽西理工学院）；Syracuse University（雪城大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Pattern Recognition Center, WeChat AI（微信人工智能模式识别中心）
We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. Our human study supports that this form of task covers comprehension of multiple types of character persona, including understanding characters' personalities, facts and memories of personal experience, which are well aligned with the psychological and literary theories about the theory of mind (ToM) of human…
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机学院）
Past work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories. However, these approaches have used heuristics or off-the-shelf models to first tag training stories with the desired type of plan, and then train generation models in a supervised fashion. In this paper, we propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process. During training, our model treats the sequence of anchor words as a latent variable and attempts to induce anchor…
"Once Upon a Time..." Literary Narrative Connectedness Progresses with Grade Level: Potential Impact on Reading Fluency and Literacy Skills
Mobile Brain（移动脑机构）；Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte (UFRN)（巴西北里奥格兰德联邦大学生物信息学多学科环境）；Árvore（阿沃雷机构）；Institute of Psychiatry (IPUB), Federal University of Rio de Janeiro (UFRJ)（里约热内卢联邦大学精神病学研究所）
Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model
Key Laboratory of Big Data and Intelligent Robot (South China University of Technology) Ministry of Education（大数据与智能机器人重点实验室（华南理工大学）教育部）；School of Software Engineering, South China University of Technology（软件学院（华南理工大学））；The Hong Kong Polytechnic University（香港理工大学）
Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. The thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on n…
UC San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）
Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with 'background stories' related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform a…
University of California Santa Cruz（加州大学圣克鲁兹分校）；Columbia University（哥伦比亚大学）
In order to tell stories in different voices for different audiences, interactive story systems require: (1) a semantic representation of story structure, and (2) the ability to automatically generate story and dialogue from this semantic representation using some form of Natural Language Generation (NLG). However, there has been limited research on methods for linking story structures to narrative descriptions of scenes and story events. In this paper we present an automatic method for converting from Scheherazade's story intention graph, a semantic representation, to the input required by th…
Natural Language and Dialogue Systems Lab（自然语言与对话系统实验室）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually authoring this dialogue can be tedious, time-consuming, or even altogether infeasible. This paper utilizes a rich narrative representation for modeling dialogue and an expressive natural language generation engine for realizing it, and expands upon a translation tool that bridges the two. We add functionality to the translator to allow direct speech to be modeled by the narrative representation, whereas the original translator supports only…
