CAREER: An Integrated Framework for Controllable Text Generation

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。这个职业项目致力于创建以文本为输入并生成修改的写作辅助系统，在保持原有含义的同时进行改进。例如，用简单的单词和语法代替复杂的单词和语法，使句子更容易理解。这将推动分析人工编辑过程和使用机器学习方法实现文档编辑自动化的最先进技术。拟议的框架可以调整以支持许多有用的应用程序，包括帮助K-12教师以适当的阅读水平为他们的学生准备教育材料，帮助STEM学生提高他们的科学写作能力，以及帮助社交媒体用户将有偏见的语言改写为中性语调。它还可以直接为儿童和低识字率或残疾人士提供阅读辅助工具，以及帮助普通公众更好地了解政府政策和医疗文件。增强学生对科学的阅读和写作能力，对于激发人们对科学事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的长期挑战，包括神经生成模型缺乏可解释性和可控性，以及缺乏针对特定任务的训练数据和可靠的评估方法。新的框架将由四个主要部分组成，其中包括：(1)高质量的数据构建，用于科学写作的新应用；(2)可控神经生成模型；(3)交互式注释界面；(4)重新设计和更可靠的评估方法。我们强调所有四个组件紧密结合的设计。生成模型将从静态文本语料库以及通过交互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进行细粒度控制。为了确保数据质量并分析人工编辑过程中的复杂性，我们将开发一种将手动注释与自动模型相结合的非平凡方法，用于基于语义对齐对应的文本片段，并对编辑的意图进行分类，以使文本生成模型更易于解释。这一框架不仅允许在向用户建议带有解释的编辑时具有更好的解释性，而且还支持针对不同用户偏好的更好的个性化。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。

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.