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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。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。这将通过开发高效且有效的会话搜索引擎来实现。现有系统通常是基于查询-响应范例来设计的，其中用户通过提交输入单词或短语来发起交互，并且系统以一个或多个文档进行响应。此过程会不断重复，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，在会话式搜索系统中，系统可能会提出澄清问题或推荐新信息，即使它不是对搜索查询的明确响应。在会话式搜索系统中，会话应该产生促进用户满意的最终目标所需的信息。提到的查询-响应范例不支持这些自然的对话交互。该职业奖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，该项目研究用于生成和处理信息搜索中的混合主动交互的理论和机器学习解决方案对话。更详细地说，该项目探讨了以下三个研究重点：（1）开发测量混合主动信息寻求对话的理论基础； (2) 开发澄清用户信息需求的模型，这被认为是最常见的混合主动交互类型； (3) 开发模型，为正在进行的对话主动提供信息。除了这些算法和建模贡献之外，该项目还开发了许多宝贵的资源来推进会话信息检索领域，包括一个会话学术助理代理，它将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。

项目成果

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: 10.1145/3539618.3591677
• 发表时间: 2023-07
• 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

统一信息访问的个性化密集检索框架

• DOI: 10.1145/3539618.3591626
• 发表时间: 2023-04-26
• 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
• 通讯作者: Hamed Zamani

Large Language Model Augmented Narrative Driven Recommendations

大语言模型增强叙事驱动的推荐

• DOI: 10.1145/3604915.3608829
• 发表时间: 2023-09
• 期刊: ACM
• 作者: Mysore, Sheshera;Mccallum, Andrew;Zamani, Hamed
• 通讯作者: Zamani, Hamed

Conversational Information Seeking

对话式信息搜寻

• DOI: 10.1561/1500000081
• 发表时间: 2023-08
• 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