CAREER: An Integrated Framework for Controllable Text Generation

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。该CAREER项目专注于创建以文本为输入并生成修订版的写作辅助系统，该系统在保留原始含义的同时得到改进。例如，用简单的单词和语法替换复杂的单词和语法，使句子更容易理解。这将推进最先进的技术，用于分析人类编辑过程，并使用机器学习方法自动化文档编辑。拟议的框架可以进行调整，以支持许多有用的应用程序，包括帮助K-12教师为学生准备适当的阅读水平的教育材料，帮助STEM学生提高他们的科学写作，并帮助社交媒体用户将有偏见的语言改写为中性语气。它还可以直接为儿童和识字率低或残疾的人提供阅读辅助，以及帮助公众更好地了解政府政策和医疗文件。赋予学生阅读和写作有关科学的权利，对于激发学生对科学职业的兴趣和支持美国经济增长非常重要。该项目将解决自然语言生成领域长期存在的挑战，包括神经生成模型缺乏可解释性和可控性，以及缺乏特定任务的训练数据和可靠的评估方法。新框架将由四个主要组成部分组成，包括：（1）科学写作新应用的高质量数据构建;（2）可控神经生成模型;（3）交互式注释界面;以及（4）重新设计和更可靠的评估方法。我们强调所有四个组成部分的紧密集成设计。生成模型将从静态文本语料库以及通过交互式界面收集的人类反馈数据中学习对单个单词和句子级别的编辑的细粒度控制。为了确保数据质量并分析人类编辑过程中的复杂性，我们将利用一种非平凡的方法，将手动注释与自动模型相结合，用于基于语义对齐相应的文本片段，并对编辑的意图进行分类，以使文本生成模型更易于解释。该框架不仅可以在向用户建议编辑时提供更好的解释性，还可以支持更好地个性化不同的用户偏好。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This CAREER project focuses on creating writing assistant systems that take text as input and generate a revision, that is improved while retaining the original meaning. For example, replacing complex words and grammar with simpler ones to make a sentence easier to understand. This will advance state-of-the-art technologies for analyzing the human editing process and automating document editing using machine learning methods. The proposed framework could be adapted to support many useful applications, including helping K-12 teachers prepare educational material for their students at an appropriate reading level, assisting STEM students to improve their scientific writing, and helping social media users to rewrite biased language into a neutral tone. It can also directly provide reading aids for children and people with low literacy or disabilities, as well as help the general public better understand government policies and medical documents. Empowering students to read and write about science is important for stimulating interest in science careers and for supporting U.S. economic growth. This project will address long-standing challenges in the field of natural language generation, including the lack of interpretability and controllability in neural generation models, and the lack of task-specific training data and reliable evaluation methods. The new framework will consist of four major components, which include: (1) high-quality data construction for a novel application of scientific writing; (2) controllable neural generation models; (3) interactive annotation interfaces; and (4) a redesigned and more reliable evaluation methodology. We emphasize the closely integrated design of all four components. The generation model will learn fine-grained control over edits at the individual word and sentence levels from static text corpora, as well as from human feedback data collected through an interactive interface. To ensure data quality and analyze the complexities in the human editing process, we will exploit a non-trivial methodology that combines manual annotations with automatic models for aligning correspondent text fragments based on semantics, and for classifying the intents of the edits to make the text generation model more explainable. This framework will not only allow better interpretability when suggesting edits to users with an explanation but also support better personalization for varied user preferences.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.