CAREER: Discourse Processing and Content Generation for Document Simplification

职业：用于文档简化的话语处理和内容生成

• 批准号: 2145479
• 负责人: Junyi Li
• 金额: $ 54.05万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145479&HistoricalAwards=false
• 关键词: CAREER; Discourse; Processing; Content; Generation

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Simplification is the process of making a text more accessible to a target audience, e.g., language learners, children, and individuals with language impairments, while preserving its meaning and content. The lack of accessible material can exacerbate social issues, for example, the complexity of language used in college admission and financial aid applications has contributed to the lagging access to higher education among emergent bilingual students; the WHO has recognized the urgency of accessible technical information, given the rise of medical misinformation especially in the wake of the COVID-19 pandemic. While there has been much work on sentence simplification, very few datasets are large enough to train supervised models; simplifying a document also involves different operations from those at the sentence level, including content addition, and how sentences connect with each other. This project aims to develop new resources and data-driven approaches for document simplification, with the potential to address information transparency and fair access across a range of high-stake domains. This project will also support the education and training of a diverse body of undergraduate and graduate students across disciplines.To substantially advance document simplification, this CAREER project will tackle several key issues in existing simplification work, including corpora diversity, explanation generation, and document-level approaches. This is achieved by the following research activities: (1) introducing new corpora that tackle the pressing challenge of data diversity in simplification research and enable new application scenarios, especially in the accessibility of technical and jargon-laden texts; (2) tackling content addition and elaboration during simplification---a previously little-explored challenge, and propose a novel, linguistically-informed framework that characterizes and generates elaborations; (3) develop models for document simplification that are informed by structures of discourse, using both coherence structure and entity salience. The innovative ways to integrate discourse target a larger challenge for models to take stretches of discourse into account.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.

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。简化是使文本更容易被目标受众所理解的过程，例如，语言学习者、儿童和有语言障碍的个人，同时保留其意义和内容。缺乏可获取的材料可能会加剧社会问题，例如，大学入学和经济援助申请中使用的语言的复杂性导致新兴双语学生接受高等教育的机会滞后;鉴于医疗错误信息的增加，特别是在COVID-19大流行之后，世卫组织已经认识到获取技术信息的紧迫性。虽然在句子简化方面有很多工作，但很少有数据集足够大，可以训练监督模型;简化文档还涉及与句子级别不同的操作，包括内容添加以及句子如何相互连接。该项目旨在开发新的资源和数据驱动的文件简化方法，有可能解决一系列高风险领域的信息透明度和公平获取问题。该项目还将支持教育和培训不同学科的本科生和研究生。为了大力推进文档简化，该CAREER项目将解决现有简化工作中的几个关键问题，包括语料库多样性，解释生成和文档级方法。这是通过以下研究活动实现的：（1）引入新的语料库，以应对简化研究中数据多样性的紧迫挑战，并实现新的应用场景，特别是在技术和术语文本的可访问性方面;（2）在简化过程中处理内容添加和细化-以前很少探索的挑战，并提出一个新的，（3）建立以语篇结构为基础的文档简化模型，同时利用连贯结构和实体显著性。整合话语的创新方法针对模型考虑话语延伸的更大挑战。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。

项目成果

Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias

反事实探究情感和特异性对群体间偏见的影响

• 发表时间: 2023
• 期刊: Findings of the Association for Computational Linguistics: ACL 2023
• 作者: Govindarajan, Venkata Subrahmanyan;Beaver, David;Mahowald, Kyle;Li, Junyi Jessy
• 通讯作者: Li, Junyi Jessy

Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections

话语理解：表示句子连接的问答框架

• 发表时间: 2022
• 期刊: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
• 作者: Ko, Wei-Jen;Dalton, Cutter;Simmons, Mark;Fisher, Eliza;Durrett, Greg;Li, Junyi Jessy
• 通讯作者: Li, Junyi Jessy

The Role of Context and Uncertainty in Shallow Discourse Parsing

• 发表时间: 2022
• 作者: Katherine Atwell;Remi Choi;Junyi Jessy Li;Malihe Alikhani

CoditT5: Pretraining for Source Code and Natural Language Editing

• DOI: 10.1145/3551349.3556955
• 发表时间: 2022-08
• 期刊: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
• 作者: Jiyang Zhang;Sheena Panthaplackel;Pengyu Nie;Junyi Jessy Li;Miloš Gligorić

Using Developer Discussions to Guide Fixing Bugs in Software

• DOI: 10.48550/arxiv.2211.06335
• 发表时间: 2022-11
• 作者: Sheena Panthaplackel;Miloš Gligorić;Junyi Jessy Li;R. Mooney