Collaborative Research: III: Small: A DREAM Proactive Conversational System

University Of Washington, Seattle WA

Investigators

Abstract

Conversational systems, such as smart assistants, are found in many of our everyday devices and contexts, including phones, smart speakers, and various online services. However, most of these systems are reactive. They only respond to what a user asks explicitly. In doing so, they rely on the user asking the right question, which disadvantages those with low information literacy. In addition, situations involving exploratory search, brainstorming, and complex decision-making require a conversation with both sides actively engaged. This project will develop an intelligent conversational agent that can not only reactively answer questions but also conduct two-way, proactive conversations, brainstorm with the user, generate new insights from the ongoing conversation, and make novel contributions to the shared dialogue. Such an intelligent conversational agent will be built on an offline hierarchical model-based deep reinforcement learning framework. This algorithmic framework's hierarchical design will provide high-level conversation strategies and the actual utterance generation. It will allow the agent to dynamically detect and determine when to chime in to take the conversation to a novel path, enriching knowledge and topics, managing multi-tiered task objectives, and assessing potential opportunities and pitfalls to propose proactive responses. This novel approach will also include state tracking and modeling that will incorporate an understanding of the conversational environmental model, proactive action planning to expand the current conversational task's knowledge and topics and proposes potential actions, as well as critiquing and validating the proposed responses that will help recognize opportunities and pitfalls to empower proactive intelligence. Evaluation of the system will involve both objective and subjective measures at the project's different phases. During the validation phase, offline experiments with existing datasets will be performed to assess and fine-tune the new learning framework. This will be followed by the crowdsourcing phase for generating ground truth and evaluating novelty and appropriateness of the conversations. Finally, the user studies phase will evaluate the system's effectiveness, as well as user experience and task performance. 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.