CCRI: ENS: Collaborative Research: Developing the Dialog Ecosystem to Support and Enhance Research in Spoken Dialog Systems

CCRI：ENS：协作研究：开发对话生态系统以支持和加强口语对话系统的研究

• 批准号: 1925576
• 负责人: David Traum
• 金额: $ 79.89万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925576&HistoricalAwards=false
• 关键词: CCRI; ENS; Collaborative; Research; Developing

People are talking to dialog systems in everyday life. Siri, Alexa and others have become household names. But as any user of these systems knows, they are far from perfect. They are also currently limited in terms of the types of things they can do. Usage data is essential to improve these systems using machine learning and artificial intelligence techniques, but companies that create dialogue systems often keep this data to themselves, making it harder for researchers to create, improve, and evaluate such systems properly. DialPort was created with the goal of gathering data from real users for dialog systems around the world. Researchers can connect their systems to the Portal or request the data that others have collected. Beyond this, for researchers who need help creating a dialog system that they could use to run studies and collect data, the DialPort DialogEcosystem provides them with access to tools for the creation of their systems and tutorials on how to use them. For researchers who already have systems and want to test them using human computation (often called crowdsourcing), the DialPort DialogEcosystem provides easy task creation and connection to major crowdsourcing sites. And to lower the barrier to entry to the field, the DialPort DialogEcosystem helps train young students with its REAL Challenge in which students can imagine ideal dialog systems and learn how to create them. The results from this project will ultimately impact every person who uses dialog systems in daily life.The DialPort project, previously funded by the Computer and Information Science and Engineering (CISE) Research Infrastructure Program, has given the Spoken Dialog Community access to tools, data and users. Researchers who want to create a new dialog system consult DialPort's website for access to the tools they need. When a dialog system is up and running, they connect it to the Portal to get real users to communicate with their systems. The DialPort DialogEcosystem will keep up with the evolving needs of a growing community in several ways. More tools and tutorials will be available. Real user data has started to flow to the systems connected to the Portal and that flow will increase substantially in order to produce the large amounts of data that are needed by state-of-the-art systems. Many researchers test early versions of their systems with crowdworkers, but they are not familiar with how to set up tasks and run quality control and they need help. DialPort DialogEcosystem will respond to these evolving needs with its DialCrowd tools. The field of researchers who are creating dialog systems is expanding. The field of Question Answering is now using chatbots as a means of testing their retrieval capabilities. In Machine Learning, researchers are working on natural language generation and finding that dialog systems are vehicles that can test their work. The DialogEcosystem will reach out to everyone who works on dialog systems and offer a complete framework that can serve their needs from creation to assessment. Beyond present researchers, the DialPort DialogEcosystem will reach out to young students to teach them about our field. The DialPort DialogEcosystem will: create a handheld version of the Portal to address how real users and workers access the Portal; greatly extend DialTools to include wrappers and tutorials for popular system creation tools, extend DialCrowd to help researchers create, assess and analyze results from Crowdwork tasks, create a new REAL Challenge to help high school and undergraduate students become familiar with our field.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.

人们正在与日常生活中的对话系统交谈。 Siri，Alexa和其他人已成为家喻户晓的名字。但是，正如这些系统的任何用户所知道的那样，它们远非完美。目前，他们在可以做的事情的类型方面也受到限制。用法数据对于使用机器学习和人工智能技术改进这些系统至关重要，但是创建对话系统的公司通常会将这些数据保留给自己，从而使研究人员更难以正确地创建，改进和评估此类系统。创建DiLport的目的是从真实用户中收集数据以获取全球对话系统的数据。研究人员可以将其系统连接到门户网站，或请求其他人收集的数据。除此之外，对于需要帮助创建对话框系统的研究人员，他们可以用来运行研究和收集数据，diLport DialogeCosystem为他们提供了访问工具以创建其系统和有关如何使用它们的教程的工具。对于已经拥有系统并希望使用人类计算（通常称为众包）测试它们的研究人员，Dialport DialogeCosystem提供了简单的任务创建和连接到主要的众包网站。为了降低进入该领域的障碍，Dialport Dialogecosystem帮助培训年轻学生的真正挑战，学生可以想象理想的对话系统并学习如何创建它们。该项目的结果最终将影响每个在日常生活中使用对话系统的人。以前由计算机和信息科学与工程（CISE）研究基础架构计划资助的DiLport项目使Spoken Dialog社区访问了工具，数据和用户。想要创建一个新的对话系统的研究人员请咨询Dialport的网站，以访问所需的工具。当对话框系统启动并运行时，他们将其连接到门户网站，以使真实的用户与他们的系统进行通信。 DiLport DialogeCosystem将以几种方式满足不断增长的社区的不断发展的需求。将提供更多工具和教程。真实的用户数据已开始流向连接到门户网站的系统，该流量将大大增加，以生成最新系统所需的大量数据。许多研究人员与人群工作人员一起测试其系统的早期版本，但他们对如何设置任务和运行质量控制并需要帮助并不熟悉。 DiLport DialogeCosystem将使用其DialCrowd工具响应这些不断发展的需求。创建对话系统的研究人员领域正在扩大。现在，问答领域是使用聊天机器人作为测试其检索功能的一种手段。在机器学习中，研究人员正在研究自然语言的生成，并发现对话系统是可以测试其工作的车辆。 DialoGecosystem将与所有在对话系统中工作的人联系，并提供一个完整的框架，可以满足他们从创建到评估的需求。除了现任研究人员之外，Dialport Dialogecosystem将与年轻学生联系，以教他们有关我们领域的知识。 Dialport DialogeCosystem将：创建门户网站的手持版本，以解决真实的用户和工人访问该门户网站的方式；大大扩展了拨号工具以包括流行系统创建工具的包装和教程，扩展了DialCrowd，以帮助研究人员从人群任务中创建，评估和分析结果，创建一个新的真正挑战，以帮助高中和本科生熟悉我们的领域。该奖项反映了NSF的法定任务，并通过评估了基金会的范围来反映支持者的支持者，并通过基金会的范围进行了评估和宽广的影响。

项目成果

Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis

• 发表时间: 2022
• 作者: A. D. Tur;D. Traum

Overview of the Ninth Dialog System Technology Challenge: DSTC9

• 发表时间: 2020-11
• 期刊: ArXiv
• 作者: Chulaka Gunasekara;Seokhwan Kim;L. F. D’Haro;Abhinav Rastogi;Yun-Nung Chen;Mihail Eric;Behnam Hedayatnia;Karthik Gopalakrishnan;Yang Liu;Chao-Wei Huang;Dilek Z. Hakkani-Tür;Jinchao Li;Qi Zhu;Lingxiao Luo;Lars Lidén;Kaili Huang;Shahin Shayandeh;Runze Liang;Baolin Peng;Zheng Zhang;Swadheen Shukla;Minlie Huang;Jianfeng Gao;Shikib Mehri;Yulan Feng;Carla Gordon;S. Alavi;D. Traum;M. Eskénazi;Ahmad Beirami;Eunjoon Cho;Paul A. Crook;Ankita De;A. Geramifard;Satwik Kottur;Seungwhan Moon;Shivani Poddar;R. Subba

Spoken language interaction with robots: Recommendations for future research

与机器人的口语交互：对未来研究的建议

• DOI: 10.1016/j.csl.2021.101255
• 发表时间: 2022
• 期刊: Computer Speech & Language
• 影响因子: 4.3
• 作者: Marge, Matthew;Espy-Wilson, Carol;Ward, Nigel G.;Alwan, Abeer;Artzi, Yoav;Bansal, Mohit;Blankenship, Gil;Chai, Joyce;Daumé, Hal;Dey, Debadeepta
• 通讯作者: Dey, Debadeepta