EAGER: A hybrid dialogue system architecture for symbolic control of deep learning networks

EAGER：用于深度学习网络符号控制的混合对话系统架构

• 批准号: 2232307
• 负责人: Barbara DiEugenio
• 金额: $ 20万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232307&HistoricalAwards=false
• 关键词: EAGER; hybrid; dialogue; system; architecture

Technological advances in the last fifteen years have brought conversational assistants like Siri and Alexa to society at large. While such assistants are successful at supporting well-specified tasks, their understanding of what the user truly wants is still limited, their answers can be incorrect, repetitive, or biased, and it is often hard to explain how they arrived at a certain answer. Their success depends on harvesting huge amounts of data and uncovering statistical patterns; however, very little data exists for conversations whose purpose is education or advice-giving, such as patient-healthcare educator interactions. This EArly Grant for Exploratory Research (EAGER) investigates novel dialogue system architectures that combine strategies designed by experts with the powerful statistical models of today, but applied to small datasets. If successful, this research will provide a new approach to develop culturally competent conversational assistants for many applications where users, especially from underrepresented groups, seek advice or knowledge.This EAGER project will explore new inference mechanisms that combine symbolic and neural approaches for the development of conversational assistants. We will develop an architecture which blends an explicitly designed dialogue manager module, with the effective neural encoder/decoder approaches of today. A symbolic dialogue manager will provide control over the outputs from the system and will help explain the system's decisions. Additionally, it will inherently ameliorate the need for a large dataset, since explicit expert insights can be used rather than emerge as latent variables. At the same time, we will explore how modern meaning representations combined with data augmentation models can be used to develop a larger corpus. The motivation for this research is culturally competent patient education, which is vital to better health outcomes especially in minority groups.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.

在过去的十五年中，技术进步将像Siri和Alexa这样的对话助手带入了整个社会。尽管此类助手成功地支持了明确指定的任务，但他们对用户真正想要的东西仍然有限的理解，他们的答案可能是不正确的，重复的或有偏见的，并且通常很难解释他们如何获得某个答案。他们的成功取决于收集大量数据和揭示统计模式。但是，对于目的是教育或提供建议的对话，例如患者保健教育者的互动很少。这项早期的探索性研究赠款（急切）调查了新颖的对话系统体系结构，这些架构将专家设计的策略与当今的强大统计模型相结合，但应用于小型数据集。如果成功，这项研究将为许多应用程序提供新的方法来开发具有文化胜任的对话助手，这些应用程序（尤其是来自代表性不足的群体中的用户）寻求建议或知识。该急切的项目将探索结合符号和神经方法的新推理机制，以发展对话助理的发展。 我们将开发一种架构，该体系结构将明确设计的对话管理器模块与当今的有效神经编码器/解码器方法融合在一起。符号对话经理将提供对系统输出的控制权，并有助于解释系统的决策。此外，由于可以使用明确的专家见解，而不是作为潜在变量出现，因此它可以固有地改善对大数据集的需求。同时，我们将探讨如何使用现代意义表示与数据增强模型相结合来开发更大的语料库。这项研究的动机是具有文化能力的患者教育，这对于更好的健康成果至关重要，尤其是在少数群体中。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的影响评估审查标准，被认为值得通过评估来提供支持。