PFI-TT: Using Artificial Intelligence (AI) and Natural Language Processing (NLP) to Enhance Writing for Publication

这一创新-技术转化伙伴关系(PFI-TT)项目的更广泛的影响/商业潜力是，新手研究作者(例如，职业生涯早期的研究人员和学生)更有机会接受高水平的科学写作培训，从而为接受赠款申请和发表同行评审的期刊文章创造一个更多样化、更公平和更具包容性的竞争环境。研究机构往往难以为他们的新手作家提供有意义的支持。这种支持通常需要昂贵得令人望而却步的人力资源，以提供关于科学写作的技术期望的培训，并且至关重要的是，评估和提供关于写作的反馈。然而，研究机构经常公开预算新的教育技术，以解决其社区内的主要痛点，为软件创造一个强大的产品市场，其中包括基于语言的科学写作教学课程。建议的技术经过培训，以识别和提供论据级别的反馈。因此，预计这项技术将通过提供关于出版写作的响应、按需指导和反馈，在一个工具中提高推进写作的能力，减轻培训负担，并加强代表不足人口获得资源的机会。该项目涉及两个主要研究机会：实施高级自动写作反馈以及使用神经网络进行文本分类和生成反馈。到目前为止，现有的提供自动反馈的写作支持工具只解决了表层的语法和机制。从理论的角度来看，神经网络在文本分类和反馈生成方面的研究还不够深入，特别是在学术写作方面。该项目预计将以两种主要方式促进知识的发展：(1)展示如何通过侧重于科学论证和描述的专门语言，在表层语法和力学的反馈之外产生自动反馈；(2)突出模式标记和机器学习的深层神经网络方法如何可用于产生针对同行审查的期刊论文和赠款申请的反馈。这些目标将通过开发自动写作反馈引擎来实现，该引擎将被集成到技术中，然后使用基于论点的框架进行验证，以评估和增强引擎的准确性和可用性。该奖项反映了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。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is greater access for novice research writers (e.g., early-career researchers and students) to high-level, responsive training in scientific writing and, as a result, a more diverse, equitable and inclusive playing field for acceptance of grant applications and publication of peer-reviewed journal articles. Research institutions often struggle to provide meaningful support for their novice writers. Such support typically requires prohibitively expensive human resources to provide training on the technical expectations of scientific writing and, crucially, to evaluate and provide feedback on writing. However, research institutions frequently have open budgets for new educational technologies that address major pain points within their community, creating a strong product-market fit for a software that includes a linguistically-grounded instructional curriculum for scientific writing. The proposed technology is trained to recognize and provide argument-level feedback. As such, the technology is expected to increase the capacity to advance writing, offset training burden, and enhance access to resources for underrepresented populations, all in one tool, by providing responsive, on-demand instruction and feedback on writing for publication. The project addresses two main research opportunities: the implementation of advanced automated writing feedback and the use of neural networks for text classification and feedback generation. To date, existing writing support tools that provide automated feedback address only surface-level grammar and mechanics. From a theoretical perspective, neural networks are underexplored for text classification and feedback generation, especially for scholarly writing. This project is expected to advance knowledge in two primary ways: (1) by demonstrating how automated feedback can be generated beyond feedback on surface-level grammar and mechanics through a focus on language specific to scientific argumentation and description and (2) by highlighting how pattern markup and a deep neural network approach to machine learning can be used to produce feedback specific to peer-reviewed journal articles and grant applications. These aims will be achieved by developing an automated writing feedback engine that will be integrated into the technology and then validated using an argument-based framework to evaluate and enhance the engine’s accuracy and usability.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.