权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

III: Small: Purposeful Conversational Agents based on Hierarchical Knowledge Graphs

III：小型：基于分层知识图的有目的的会话代理

基本信息

批准号：
2214070
负责人：
Rohini Srihari
金额：
$ 56.69万
依托单位：
SUNY at Buffalo
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：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2214070&HistoricalAwards=false
关键词：
III Small Purposeful Conversational Agents

项目摘要

There has been tremendous progress recently in the area of open domain conversational agents (CA), also referred to as chatbots or socialbots, by leveraging neural response generators and large, diverse training corpora. The recent Alexa Prize competition resulted in socialbots that can engage in prolonged conversations on any topic, paving the way for meaningful use. These systems are limited to chitchat, where the conversation meanders from topic to topic without any specific goal. There is now an opportunity (and need) to develop models for purposeful conversations, motivated by societal goals. This facilitates compelling solutions such as chatbots to assist with mental health issues, providing companionship to senior citizens, and even combating disinformation. CAs can be configured for different personalities, diverse and multilingual populations and thus hold great potential to address societal problems at scale. Realizing this goal requires advances on multiple fronts, including ethical issues and trustworthiness. This project addresses two key technical challenges. First, the project eliminates artifacts of neural response generation such as inconsistency, incoherence, and repetition. These responses diminish the trustworthiness of socialbots and hence their impact. Second, the project addresses the computational frameworks for purposeful conversations, specifically, persuasion. This project will result in a fully functional CA, that incorporates factual knowledge, logical reasoning, and conversational strategies found in human-to-human dialogue, to engage in coherent, interesting and persuasive conversations across several topics related to societal issues.Purposeful conversational agents must engage in meaningful exchanges with a motive: a persuasive conversation may cycle through inquiry, negotiation, or deliberation interspersed with chit-chat and anecdotes. In order to realize these objectives, this project incorporates the use of specially constructed multi-layered knowledge graphs, along with state-of-the-art neural response generators, intent classifiers, and a robust natural language understanding and dialogue management module. The core layers of the open knowledge network consist of consolidated concepts and relationships from existing open knowledge networks and causal graphs, suitably enhanced to facilitate neuro-symbolic approaches for knowledge grounded conversation. The peripheral layers constitute argumentation graphs which are automatically constructed through processing existing corpora of persuasive conversations and extracting elemental discourse units and relationships between them. An innovative neuro-symbolic approach is necessary to (i) avoid the typical “hallucinations” exhibited by neural response generators alone and (ii) intelligently navigate through the various stages of a persuasive conversation. The neural response generators are configured to efficiently retrieve, reason and incorporate information from the open knowledge network, in order to generate factually correct, coherent, engaging and persuasive responses. Both novel and traditional metrics will be used in order to assess the quality of the generated conversations and measure its impact. Automated evaluation will be conducted on representative data sets; the prototypes will also be evaluated by experts who will provide feedback regarding viability for future deployment.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.

最近在开放域会话代理（CA）领域取得了巨大进展，也被称为聊天机器人或社交机器人，通过利用神经响应生成器和大型多样化的训练语料库。最近的Alexa Prize竞赛产生了社交机器人，它们可以就任何话题进行长时间的对话，为有意义的使用铺平了道路。这些系统仅限于闲聊，谈话从一个话题到另一个话题，没有任何特定的目标。现在有一个机会（也有必要）来开发由社会目标驱动的有目的的对话模式。这促进了令人信服的解决方案，例如聊天机器人，以帮助解决心理健康问题，为老年人提供陪伴，甚至打击虚假信息。 CA可以针对不同的个性、多样性和多语言人群进行配置，因此具有解决大规模社会问题的巨大潜力。实现这一目标需要在多个方面取得进展，包括道德问题和可信度。该项目解决了两个关键的技术挑战。首先，该项目消除了神经反应生成的人为因素，如不一致，不连贯和重复。这些反应降低了社交机器人的可信度，从而降低了它们的影响力。其次，该项目解决了有目的的对话，特别是说服的计算框架。该项目将产生一个功能齐全的CA，它结合了人与人对话中的事实知识，逻辑推理和会话策略，以参与与社会问题相关的多个主题的连贯，有趣和有说服力的对话。有目的的会话代理必须参与有动机的有意义的交流：一次有说服力的谈话可以通过询问、谈判或深思熟虑来进行，其间穿插着闲聊和轶事。为了实现这些目标，该项目采用了专门构建的多层知识图，沿着最先进的神经反应生成器，意图分类器以及强大的自然语言理解和对话管理模块。开放知识网络的核心层由来自现有开放知识网络和因果图的合并概念和关系组成，适当地增强以促进基于知识的对话的神经符号方法。外围层构成论证图，这些论证图是通过处理现有说服性会话语料库并提取基本话语单元及其之间的关系来自动构建的。一种创新的神经象征方法是必要的，以（i）避免典型的“幻觉”所表现出的神经反应发生器单独和（ii）智能导航通过各个阶段的说服性谈话。神经响应生成器被配置为有效地检索、推理和合并来自开放知识网络的信息，以便生成事实上正确的、连贯的、吸引人的和有说服力的响应。将使用新的和传统的指标来评估生成的对话的质量并衡量其影响。自动评估将在代表性数据集上进行;原型也将由专家进行评估，专家将提供有关未来部署可行性的反馈。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知识价值和更广泛的影响审查标准进行评估来支持。