CAREER: Enriching Conversational Information Retrieval via Mixed-Initiative Interactions

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

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.