CAREER: Enriching Conversational Information Retrieval via Mixed-Initiative Interactions

职业：通过混合主动交互丰富对话信息检索

• 批准号: 2143434
• 负责人: Hamed Zamani
• 金额: $ 57.09万
• 依托单位: University of Massachusetts Amherst
• 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: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143434&HistoricalAwards=false
• 关键词: CAREER; Enriching; Conversational; Information; Retrieval

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).It has become clear that providing access to information through natural language conversations will play a significant role in the future of search technology. This will be enabled by developing efficient and effective conversational search engines. Existing systems are generally designed based on a query-response paradigm, in which the user initiates the interaction by submitting typing a word or phrase, and the system responds with one or more documents. This process repeats itself until the user either receives a useful response or terminates the search session. This is not an optimal design for interaction. A better approach would be to create search systems that operate like a conversation. In a conversational search systems, for instance, the system may ask a clarifying question or can recommend new information even though it is not an explicit response to the search query. A conversational search system, the conversation should yield the information that is needed to facilitate the ultimate goal of user satisfaction. The mentioned query-response paradigm does not support these natural conversational interactions. This CAREER award aims to advance the state-of-the-art by envisioning solutions that go beyond this query-response paradigm.To achieve this goal, this project studies theoretical and machine learning solutions for generating and handling mixed-initiative interactions in information seeking conversations. In more detail, this project explores the following three research thrusts: (1) developing theoretical foundations for measuring mixed-initiative information seeking conversations; (2) developing models for clarifying the user's information needs which is considered as the most common mixed-initiative interaction type; and (3) developing models for proactive informational contributions to ongoing conversations. In addition to these algorithmic and modeling contributions, this project also develops a number of invaluable resources for advancing the field of conversational information retrieval, including a conversational scholarly assistant agent that will be used as a tool for online experimentation and public data creation.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。很明显，通过自然语言对话提供信息访问将在搜索技术的未来发挥重要作用。这将通过开发高效和有效的对话搜索引擎来实现。现有系统通常基于查询-响应范例来设计，其中用户通过提交键入单词或短语来发起交互，并且系统用一个或多个文档来响应。这个过程会不断重复，直到用户收到有用的响应或终止搜索会话。这不是交互的最佳设计。更好的方法是创建像对话一样运行的搜索系统。例如，在对话式搜索系统中，系统可以提出澄清问题，或者可以推荐新信息，即使它不是对搜索查询的明确响应。一个对话式搜索系统，对话应该产生所需的信息，以促进用户满意度的最终目标。上面提到的查询-响应范例不支持这些自然的会话交互。这个CAREER奖项旨在通过设想超越这种查询-响应范式的解决方案来推进最先进的技术。为了实现这一目标，该项目研究了理论和机器学习解决方案，用于在信息寻求对话中生成和处理混合主动交互。更详细地说，本项目探讨了以下三个研究重点：（1）发展测量混合主动信息寻求对话的理论基础;（2）发展模型，以澄清用户的信息需求，这被认为是最常见的混合主动互动类型;（3）发展模型，积极主动的信息贡献正在进行的对话。除了这些算法和建模的贡献，该项目还开发了一些宝贵的资源，以推进对话信息检索领域，包括一个对话学术助理代理，将被用作在线实验和公共数据创建的工具。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智力价值和更广泛的影响审查标准。

项目成果

Conversational Information Seeking

对话式信息搜寻

• DOI: 10.1561/1500000081
• 发表时间: 2023
• 期刊: Foundations and Trends® in Information Retrieval
• 作者: Zamani, Hamed;Trippas, Johanne R.;Dalton, Jeff;Radlinski, Filip
• 通讯作者: Radlinski, Filip

Large Language Model Augmented Narrative Driven Recommendations

大语言模型增强叙事驱动的推荐

• DOI: 10.1145/3604915
• 发表时间: 2023
• 期刊: ACM
• 作者: Mysore, Sheshera;McCallum, Andrew;Zamani, Hamed
• 通讯作者: Zamani, Hamed

Editable User Profiles for Controllable Text Recommendations

用于可控文本推荐的可编辑用户配置文件

• DOI: 10.1145/3539618
• 发表时间: 2023
• 期刊: ACM
• 作者: Mysore, Sheshera;Jasim, Mahmood;McCallum, Andrew;Zamani, Hamed
• 通讯作者: Zamani, Hamed

A Personalized Dense Retrieval Framework for Unified Information Access

• DOI: 10.1145/3539618.3591626
• 发表时间: 2023-04
• 期刊: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Hansi Zeng;Surya Kallumadi;Zaid Alibadi;Rodrigo Nogueira;Hamed Zamani