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.

过去 15 年的技术进步为整个社会带来了 Siri 和 Alexa 等会话助理。虽然此类助手成功地支持明确的任务，但他们对用户真正想要什么的理解仍然有限，他们的答案可能不正确、重复或有偏见，而且通常很难解释他们如何得出某个答案。他们的成功取决于收集大量数据并揭示统计模式；然而，以教育或提供建议为目的的对话（例如患者与医疗保健教育者互动）的数据非常少。这项早期探索性研究资助 (EAGER) 研究了新颖的对话系统架构，该架构将专家设计的策略与当今强大的统计模型相结合，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开发具有文化能力的对话助理提供一种新方法，在这些应用程序中，用户，特别是来自代表性不足的群体，寻求建议或知识。这个 EAGER 项目将探索新的推理机制，将符号和神经方法结合起来，以开发会话助理。 我们将开发一种架构，将明确设计的对话管理器模块与当今有效的神经编码器/解码器方法相结合。符号对话管理器将提供对系统输出的控制，并帮助解释系统的决策。此外，它本质上会改善对大型数据集的需求，因为可以使用明确的专家见解，而不是作为潜在变量出现。同时，我们将探索如何将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。这项研究的动机是对患者进行文化上的教育，这对于更好的健康结果至关重要，尤其是少数群体。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。