A persona aware persuasive dialogue policy for dynamic and co-operative goal setting. (1st June 2022) - Tiwari, Abhisek; Saha, Tulika; Saha, Sriparna; Sengupta, Shubhashis; Maitra, Anutosh; Ramnani, Roshni; Bhattacharyya, Pushpak
Abstract: Contextualization: In recent years, the popularity of virtual agents particularly task-oriented dialogue agents has increased immensely due to their effectiveness and simplicity in various domains such as industry, e-commerce, and health. Problem: In real-world, users do not have always a predefined and immutable goal, i.e., they may upgrade/downgrade/update their task goal dynamically depending upon their utility and the serving capability of the assisting agent. However, the existing Virtual Agents (VAs) in the dialogue literature relinquish and yield dialogue failure if they find any dynamic goal setting or goal unavailability scenarios. Contributions and methodology: Motivated by these inabilities of existing VAs, we propose some intelligent and expert Dialogue Agents (A Unified Dialogue Agent and Multi-agent Dialogue system) that can deal with dynamic and goal unavailability situations to elevate both user satisfaction and the agent's utility particularly task success rate. The proposed architecture incorporates a goal guiding module namely Dynamic and Co-Operative Goal Driven Module ( DyCoGDM ), which traces goal status and resolves goal discrepancy through dynamic goal setting ( Goal Formulator ) and personalized persuasion ( Goal Persuader ) mechanisms. We also created and annotated a dialogue corpus because of unavailability of such corpus featured with dynamic and goal unavailability scenarios. Findings and implications: Our proposed method outperforms several baselines and state of the art methods in all evaluation metrics. The proposed VA is capable of dealing with dynamic goals and goal unavailability scenarios effectively. The study found that the persona aware persuasive dialogue agent outperforms generalized persuasive dialogue agent by a large margin. Furthermore, we also observed that the task oriented reward is the most essential reward for training a reinforcement learning based agent and agents trained without task based reward do not even converge. Highlights: Existing VAs abort the conversation during goal unavailability situations. Proposal of novel DM architecture and joint reward (task, sentiment & persona) model. Design of both unified and multi-agent persona aware persuasive policies. Proposed VA elevates both user satisfaction & agent utility. Easy adaptation to other task-oriented dialogue settings.