CRII: HCC: Interactive Natural Language Technology for Supporting Writers in Structuring and Revising Documents

CRII：HCC：支持作者构建和修改文档的交互式自然语言技术

• 批准号: 2246145
• 负责人: Kenneth Arnold
• 金额: $ 17.5万
• 依托单位: Calvin University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246145&HistoricalAwards=false
• 关键词: CRII; HCC; Interactive; Natural; Language

CRII: HCC: Interactive Natural Language Technology for Supporting Writers in Structuring and Revising DocumentsWritten communication and documentation empower collaboration, decision-making, and execution. It is challenging to write documents that are well structured and serve the needs of readers. Tools like Grammarly help fix errors in grammar within single sentences. But they are not designed to help structure paragraphs or documents to serve an audience. Although AI systems (such as ChatGPT) can help writers, they generate text that is not in the author’s own words. The content that comes out can be uninformative, not useful, or effectively plagiarized. These problems mean that many organizations now limit the use of generative technology in content that they publish. This project explores how computer systems can help writers structure their documents. The system would not generate words for writers. Instead, it helps writers craft well-structured documents in their own words. The project will explore how to add features to word processing applications. These features will help writers organize their thoughts and structure their writing. The features may also help predict what questions and confusions that readers might have. The research team is a diverse group of undergraduate students in various majors. They will improve their skills in building tools that use artificial intelligence (AI). These tools will help people improve their writing and communication. The project seeks to study how human writers can interact with intelligent systems during the revision stage. This stage occurs between the initial composition and the final editing for mechanics. The researchers have already prototyped several possible interactions. The team will begin by completing the implementation of these interactions. The team will then refine and test those implementations. Testing will include simulated and authentic revision contexts. Participants for these evaluations will be mainly university students and faculty. Participants will perform typical writing tasks expected for classes and publications. The perspectives of both the writer and of the reader will determine success. Evaluation of writer experience will result from post-task interviews and analysis of interaction logs. Evaluation by readers may include measures of accuracy, structure, and originality. The research will use pre-trained deep neural networks for initial prototypes. This is the same approach that powers ChatGPT. Data collected from experiments with writers will help the researchers improve language technology. They may use prompt engineering, fine-tuning, or other approaches. This project should produce fundamental knowledge about how generative AI systems can help writers. The project also should produce practical open-source tools to improve communication.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.

CRII：HCC：交互式自然语言技术，用于支持作家构建和修订文档的沟通和文档授权协作，决策和执行。编写结构良好并满足读者需求的文档是挑战。语法等工具有助于修复单个句子中语法中的错误。但是它们并非旨在帮助构建段落或文档来为受众提供服务。尽管AI系统（例如ChatGpt）可以帮助作家，但它们生成不在作者自己的话语中的文本。出来的内容可能是无关紧要的，没有用，没有用，或者有效地遭到欺骗。这些问题意味着许多组织现在限制了他们发布的内容中对通用技术的使用。本项目探讨了计算机系统如何帮助作者构建其文档。该系统不会为作家生成单词。取而代之的是，它可以用自己的语言来帮助作家制作结构良好的文档。该项目将探索如何在文字处理应用程序中添加功能。这些功能将帮助作家组织他们的思想并构造他们的写作。这些功能还可以帮助预测读者可能会遇到的问题和混乱。研究小组是一群学生，各个专业的本科生。他们将提高他们在使用人工智能（AI）的工具方面的技能。这些工具将帮助人们改善他们的写作和沟通。该项目旨在研究在修订阶段，人类作家如何与智能系统互动。此阶段发生在初始组成与力学的最终编辑之间。研究人员已经对几种可能的相互作用进行了原型。团队将首先完成这些交互的实施。然后，团队将完善并测试这些实现。测试将包括模拟和真实的修订环境。这些评估的参与者将主要是大学生和教职员工。参与者将执行班级和出版物期望的典型写作任务。作者和读者的观点都将决定成功。对作者经验的评估将是由任务后访谈和互动日志的分析产生的。读者的评估可能包括准确性，结构和独创性的度量。该研究将使用预先训练的深神经网络进行初始原型。这是供电的方法。从作家实验中收集的数据将有助于研究人员改善语言技术。他们可以使用及时的工程，微调或其他方法。该项目应提供有关通用AI系统如何帮助作家的基本知识。该项目还应该生产实用的开源工具来改善沟通。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估诚实的支持。