CAREER: Data-Driven Document Generation

职业：数据驱动文档生成

• 批准号: 2037519
• 负责人: Alexander Rush
• 金额: $ 50万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037519&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Document; Generation

Increasingly, machine-generated language is being used in everyday interactions, from consumer applications such as intelligent assistants to accessibility tools for simplifying complex documents. This CAREER project seeks to improve methods for text generation to ensure they work consistently when producing documents longer than a few sentences. The investigator will study machine learning-based methods for text generation that aim to ensure that document structure is consistent, that people and places are referred to appropriately, and that factual information is correctly expressed. The award will additionally support the development of open-source tools to make it easier for others to use and extend these approaches for a variety of text generation application. This research will be integrated into education through new methods for university teaching of machine learning, mentoring for underrepresented students in computer science, and outreach to primary-school students who may not have been exposed to artificial intelligence and data science as an area of study. The focus of this project is on data-driven natural language generation with deep learning. In recent years, the field has made progress on using new machine learning methods to learn to generate text based on human examples; however, this work is still far from human level particularly when generating document-level text. This project will develop new machine learning methods using deep generative models. These include learning and controlling document structure with deep hidden Markov models, content determination through generative reference and alignment, and aggregating content into text through bottom-up selection. Each of these combines research into machine learning methods with natural language analysis. The goal of the project is to develop an open-source implementation of a text generation methods that allow users to easily target new domains and assess the fidelity and coherence of their system.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.

机器生成语言越来越多地用于日常交互，从智能助理等消费者应用程序到简化复杂文档的辅助工具。这个CAREER项目旨在改进文本生成的方法，以确保在生成超过几句话的文档时，它们能够始终如一地工作。研究人员将研究基于机器学习的文本生成方法，旨在确保文档结构一致，人员和地点被适当引用，以及事实信息被正确表达。该奖项还将支持开源工具的开发，使其他人更容易使用和扩展这些方法用于各种文本生成应用程序。这项研究将通过大学机器学习教学的新方法，为计算机科学中代表性不足的学生提供指导，以及向可能没有接触过人工智能和数据科学作为研究领域的小学生提供服务，从而融入教育。该项目的重点是通过深度学习生成数据驱动的自然语言。近年来，该领域在使用新的机器学习方法来学习基于人类示例生成文本方面取得了进展;然而，这项工作仍然远远没有达到人类水平，特别是在生成文档级文本时。该项目将使用深度生成模型开发新的机器学习方法。这些包括使用深度隐马尔可夫模型学习和控制文档结构，通过生成参考和对齐确定内容，以及通过自下而上选择将内容聚合为文本。每一个都将机器学习方法的研究与自然语言分析相结合。该项目的目标是开发一个文本生成方法的开源实现，使用户能够轻松地定位新的域并评估其系统的保真度和一致性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Symbolic Planning and Code Generation for Grounded Dialogue

扎根对话的符号规划和代码生成

• DOI: 10.18653/v1/2023.emnlp-main.460
• 发表时间: 2023
• 期刊: Association for Computational Linguistics
• 作者: Chiu, Justin;Zhao, Wenting;Chen, Derek;Vaduguru, Saujas;Rush, Alexander;Fried, Daniel
• 通讯作者: Fried, Daniel

Model Criticism for Long-Form Text Generation

长文本生成的模型批评

• DOI: 10.18653/v1/2022.emnlp-main.815
• 发表时间: 2022
• 期刊: Association for Computational Linguistics
• 作者: Deng, Yuntian;Kuleshov, Volodymyr;Rush, Alexander
• 通讯作者: Rush, Alexander

Scaling Hidden Markov Language Models

• DOI: 10.18653/v1/2020.emnlp-main.103
• 发表时间: 2020-11
• 作者: Justin T Chiu;Alexander M. Rush

Posterior Control of Blackbox Generation

• DOI: 10.18653/v1/2020.acl-main.243
• 发表时间: 2020-05
• 期刊: Public Relations Review
• 影响因子: 4.2
• 作者: Xiang Lisa Li;Alexander M. Rush

Sequence-level Mixed Sample Data Augmentation

• DOI: 10.18653/v1/2020.emnlp-main.447
• 发表时间: 2020-11
• 作者: Demi Guo;Yoon Kim;Alexander M. Rush