CAREER: Synergistic Approaches for Specialized Intelligent Assistance

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。智能辅助系统目前缺乏在特定领域自动提供有效响应所需的深入知识，例如社交媒体上的情感支持。手动创建专门的知识库或一刀切的模型既昂贵又不可行。现有的智能辅助系统研究解决了用户建模、信息提取和文本生成这三个重要的子问题，但认为这些问题是独立的，因此使用独立的方法来解决它们。基本的假设是，不需要交叉利用需要处理的信息或通过处理每个子问题而学到的知识。例如，知识库或知识图不需要或几乎不需要通过信息抽取方法进行扩展就可以获得所有的事实。同样，语言模型应该经过了良好的训练，可以生成事实问题的答案，而不需要来自其他方法的信息。这一基本假设过于简单化，不适用于专门的智能援助。该项目通过在用户建模、信息提取和文本生成方面发现和利用协同效应来解决这一限制。PI设计、开发和评估新的算法，以帮助患有焦虑、抑郁和其他类型精神问题并在社交媒体上寻求帮助的个人。此外，这项研究还支持圣母大学不同的博士和本科生群体的跨学科发展。提出的算法通过共享知识相互增强。该项目的技术目标分为三个方面。第一个推动力是开发新的信息提取方法，以加强从社交媒体数据中构建心理健康本体。这些方法将非结构化社交媒体数据转换为结构化数据，以实现高效的检索和学习。第二个推动力开发了新的自然语言生成技术，以使用用户模型和本体创建文本响应，从而实现个性化和知识感知。第三个推力通过从用户行为数据和结构化知识中学习的新颖的情境化表示学习算法来增强用户模型。建议的算法保留了用户及其生成的内容的时空行为模式，以更准确地反映他们的情况和需求。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Intelligent assistance systems currently lack the in-depth knowledge needed to automatically provide effective responses in specialized domains, such as emotional support on social media. Manually creating specialized knowledge bases or a one-fits-all model is expensive and infeasible. Existing research on intelligent assistance systems tackles three important sub-problems, user modeling, information extraction, and text generation, but considers these problems to be separate and so addresses them with separate methods. The underlying assumption is that there is no need for cross-utilization of the information needed to address or the knowledge learned by addressing each sub-problem. For instance, knowledge bases or knowledge graphs need no or little expansion by information extraction methods to obtain all the facts. Similarly, language models would have been well trained for generating an answer to the factual question and need no information from the other methods. This underlying assumption is too simplistic and does not hold for specialized intelligent assistance. This project addresses this limitation by discovering and utilizing synergies in user modeling, information extraction, and text generation. The PI designs, develops, and evaluates novel algorithms to assist individuals who are suffering from anxiety, depression, and other types of mental issues and who are seeking help on social media. Furthermore, this research supports the cross-disciplinary development of a diverse cohort of PhD and undergraduate students at Notre Dame.The proposed algorithms mutually enhance one another by sharing knowledge. The technical aims of the project are divided into three thrusts. The first thrust develops novel information extraction methods to enhance the construction of mental health ontologies from social media data. These methods convert unstructured social media data into structured data for efficient retrieval and learning. The second thrust develops novel natural language generation techniques to create textual responses with user models and ontologies, enabling personalization and knowledge awareness. The third thrust enhances user models with novel contextualized representation learning algorithms that learn from user behavior data and structured knowledge. The proposed algorithms preserve the spatio-temporal behavioral patterns of users and their generated content to more precisely reflect their situations and needs.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.