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CAREER: Synergistic Approaches for Specialized Intelligent Assistance

职业：专业智能援助的协同方法

基本信息

批准号：
2142827
负责人：
Meng Jiang
金额：
$ 55万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-15 至 2027-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142827&HistoricalAwards=false
关键词：
CAREER Synergistic Approaches Specialized Intelligent

项目摘要

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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。智能辅助系统目前缺乏在特定领域（如社交媒体上的情感支持）自动提供有效响应所需的深入知识。手动创建专门的知识库或一刀切的模型是昂贵且不可行的。现有的智能辅助系统研究主要涉及用户建模、信息提取和文本生成三个重要的子问题，但都认为这些问题是相互独立的，采用不同的方法来解决。潜在的假设是，不需要交叉利用解决每个子问题所需的信息或通过解决每个子问题学到的知识。例如，知识库或知识图不需要或很少需要通过信息提取方法进行扩展以获得所有事实。类似地，语言模型将被很好地训练为生成事实问题的答案，并且不需要来自其他方法的信息。这种潜在的假设过于简单化，并不适用于专门的智能辅助。本项目通过发现和利用用户建模、信息提取和文本生成中的协同作用来解决这一限制。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的人，以及在社交媒体上寻求帮助的人。此外，这项研究支持了圣母大学多样化的博士和本科生的跨学科发展。所提出的算法通过共享知识来相互增强。该项目的技术目标分为三个重点。第一个重点是开发新的信息提取方法，以增强从社交媒体数据中构建心理健康本体的能力。这些方法将非结构化的社交媒体数据转换为结构化数据，以便有效地检索和学习。第二个重点是开发新的自然语言生成技术，用用户模型和本体创建文本响应，实现个性化和知识感知。第三个重点是通过从用户行为数据和结构化知识中学习的新颖情境化表示学习算法来增强用户模型。该算法保留了用户及其生成内容的时空行为模式，以更准确地反映用户的情况和需求。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions

IfQA：反事实预设下的开放域问答数据集

DOI：
10.18653/v1/2023.emnlp-main.515
发表时间：
2023
期刊：
EMNLP
影响因子：
0
作者：
Yu, Wenhao;Jiang, Meng;Clark, Peter;Sabharwal, Ashish
通讯作者：
Sabharwal, Ashish

Generate rather than Retrieve: Large Language Models are Strong Context Generators

DOI：
10.48550/arxiv.2209.10063
发表时间：
2022-09
期刊：
ArXiv
影响因子：
0
作者：
W. Yu;Dan Iter;Shuohang Wang;Yichong Xu;Mingxuan Ju;Soumya Sanyal;Chenguang Zhu;Michael Zeng;Meng Jiang
通讯作者：
W. Yu;Dan Iter;Shuohang Wang;Yichong Xu;Mingxuan Ju;Soumya Sanyal;Chenguang Zhu;Michael Zeng;Meng Jiang

Semi-Supervised Graph Imbalanced Regression

DOI：
10.1145/3580305.3599497
发表时间：
2023-05
期刊：
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Gang Liu;Tong Zhao;Eric Inae;Te Luo;Meng Jiang
通讯作者：
Gang Liu;Tong Zhao;Eric Inae;Te Luo;Meng Jiang

Completing Taxonomies with Relation-Aware Mutual Attentions