CAREER: Learning Multi-Level Narrative Structure

职业：学习多层次叙事结构

• 批准号: 1749917
• 负责人: Mark Finlayson
• 金额: $ 55万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749917&HistoricalAwards=false
• 关键词: CAREER; Learning; Multi; Level; Narrative

Why do certain stories, but not others, resonate so powerfully with certain populations? Stories (a.k.a. narratives) are powerful: where rational argument fails, a single story can drive home a point, change a mind, and even change a life. What specific structures underlie the power of narrative, and what new artificial intelligence (AI) techniques are needed to learn these structures automatically so we can leverage them in applications? This project seeks to develop these new AI techniques to automatically uncover and confirm the fundamental structures underlying narrative, developing and testing with data drawn from the domains of education and culture. This work will be of broad relevance to developing more intelligent machines, understanding the mind and brain, and improving education. It will produce fundamental insights into a universal form of communication (narrative), providing a potentially transformative new set of tools to researcher and educators.The project will develop new machine learning and natural language processing approaches to learning key aspects of narrative structure. The basic structure of a narrative involves the plot, a time-ordered sequence of important events, and the plot can be divided into three levels of structure: (1) plot pieces, (2) archetypal characters, and (3) narrative arcs. The PI and his students will first learn to extract these three types of narrative structure, the third of which (narrative arcs) is as-yet untried, using novel combinations of existing grammar learning approaches and Bayesian approaches, specifically the PI's Analogical Story Merging (ASM) algorithm, the Infinite Relational Model (IRM), and iterative learning. Second, the researchers will test hypotheses that reflect why specific stories are persuasive to specific cultures, and apply these insights to improving minority engagement in STEM and computing in middle-school classrooms in Miami Dade County Public Schools. Third, the researchers will seek to uncover systematic regularities in professional education cases (such as business cases, or medical case reports) that will lead to the ability to make computational predictions as to which cases should be most effective in the classroom.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为什么某些故事，而不是其他故事，与某些人群产生如此强烈的共鸣？故事（又名叙事）是强大的：在理性论证失败的地方，一个单一的故事可以让人明白一个观点，改变一个想法，甚至改变一个人的生活。什么样的具体结构是叙事能力的基础，需要什么样的新人工智能（AI）技术来自动学习这些结构，以便我们可以在应用程序中利用它们？该项目旨在开发这些新的人工智能技术，以自动发现和确认叙事背后的基本结构，并利用来自教育和文化领域的数据进行开发和测试。这项工作将对开发更智能的机器，理解思维和大脑以及改善教育具有广泛的相关性。该项目将为研究人员和教育工作者提供一套具有潜在变革性的新工具，并将开发新的机器学习和自然语言处理方法，以学习叙事结构的关键方面。叙事的基本结构包括情节，即按时间顺序排列的一系列重要事件，情节可以分为三个层次的结构：（1）情节片段，（2）原型人物，（3）叙事弧。PI和他的学生将首先学习提取这三种类型的叙事结构，其中第三种（叙事弧）尚未尝试，使用现有语法学习方法和贝叶斯方法的新颖组合，特别是PI的类比故事合并（ASM）算法，无限关系模型（NRM）和迭代学习。其次，研究人员将测试反映为什么特定故事对特定文化有说服力的假设，并将这些见解应用于改善迈阿密戴德县公立学校中学课堂中少数民族对STEM和计算的参与。第三，研究人员将寻求发现专业教育案例（如商业案例或医疗案例报告）中的系统性知识，从而能够对哪些案例在课堂上最有效进行计算预测。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Detecting Subevents using Discourse and Narrative Features

• DOI: 10.18653/v1/p19-1471
• 发表时间: 2019-07
• 作者: Mohammed Aldawsari;Mark A. Finlayson

Story Fragment Stitching: The Case of the Story of Moses

• 发表时间: 2020
• 作者: Mohammed Aldawsari;Ehsaneddin Asgari;Mark A. Finlayson

Confirming the Generalizability of a Chain-Based Animacy Detector

确认基于链的动画检测器的通用性

• 发表时间: 2021
• 期刊: 1st Workshop on Artificial Intelligence for Narratives (AI4N 2020
• 作者: Jahan, Labiba;Yarlott, W. Victor;Rahul, Mittal;Finlayson, Mark A.
• 通讯作者: Finlayson, Mark A.

Distinguishing Between Foreground and Background Events in News

• DOI: 10.18653/v1/2020.coling-main.453
• 发表时间: 2020-12
• 作者: Mohammed Aldawsari;Adrián Pérez;Deya Banisakher;Mark A. Finlayson

Improving the Identification of the Discourse Function of News Article Paragraphs

• DOI: 10.18653/v1/2020.nuse-1.3
• 发表时间: 2020-07
• 作者: Deya Banisakher;W. V. Yarlott;Mohammed Aldawsari;N. Rishe;Mark A. Finlayson