Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
This work addresses the need for more interactive and effective digital assistants for users performing real-world tasks like cooking, though it appears incremental as it builds on existing dialogue system components and competition participation.
The authors tackled the problem of guiding users through complex multi-step tasks by introducing TacoBot, a collaborative and engaging task-oriented dialogue system, which achieved third place in the Alexa Prize TaskBot Challenge among ten teams.
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps. Covering a wide range of cooking and how-to tasks, we aim to deliver a collaborative and engaging dialogue experience. Equipped with language understanding, dialogue management, and response generation components supported by a robust search engine, TacoBot ensures efficient task assistance. To enhance the dialogue experience, we explore a series of data augmentation strategies using LLMs to train advanced neural models continuously. TacoBot builds upon our successful participation in the inaugural Alexa Prize TaskBot Challenge, where our team secured third place among ten competing teams. We offer TacoBot as an open-source framework that serves as a practical example for deploying task-oriented dialogue systems.