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：支持作者构建和修改文档的交互式自然语言技术写作交流和文档增强协作、决策和执行的能力。编写结构良好并满足读者需求的文档是一项具有挑战性的工作。像Grammarly这样的工具可以帮助纠正单个句子中的语法错误。但它们的设计并不是为了帮助组织段落或文档以服务于受众。虽然人工智能系统(如ChatGPT)可以帮助作家，但它们产生的文本不是作者自己的话。出来的内容可能没有信息，没有用处，或者实际上被抄袭了。这些问题意味着，许多组织现在限制在他们发布的内容中使用生成性技术。这个项目探索计算机系统如何帮助作者组织他们的文档。该系统不会为作者生成单词。相反，它帮助作者用自己的语言制作结构良好的文档。该项目将探索如何为字处理应用程序添加功能。这些特点将帮助作家组织他们的思想和结构他们的写作。这些功能还可以帮助预测读者可能会有什么问题和困惑。研究团队是由不同专业的本科生组成的多元化群体。他们将提高使用人工智能(AI)构建工具的技能。这些工具将帮助人们提高他们的写作和沟通能力。该项目旨在研究人类作家如何在修改阶段与智能系统互动。这一阶段发生在力学的初始合成和最终编辑之间。研究人员已经制作了几种可能的相互作用的原型。团队将从完成这些交互的实现开始。然后，该团队将改进和测试这些实现。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。参与者将完成课堂和出版物预期的典型写作任务。作者和读者的观点将决定成功与否。对作者经验的评估将通过任务后访谈和互动日志分析得出。读者的评价可能包括准确性、结构和原创性的衡量标准。这项研究将使用预先训练好的深度神经网络作为初始原型。这与支持ChatGPT的方法相同。从与作家的实验中收集的数据将帮助研究人员提高语言技术。他们可以使用即时工程、微调或其他方法。这个项目应该产生关于生成性人工智能系统如何帮助作家的基本知识。该项目还应该制作实用的开源工具来改善沟通。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。