Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System (2023)

Abstract: 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.

## introduction task-oriented dialogue (tod) systems have shown promise in achieving user goals through conversational interactions @xcite @xcite . however, existing tod systems focus on users providing information while the system performs tasks. in contrast, our task bot assists users in executing tasks themselves by providing accurate information and guidance. however, we face several challenges, including the following: (1) existing tod systems prioritize functional goals at the expense of user experience. our contributions include: (1) developing a modularized tod framework with accurate language understanding, flexible dialogue management, and engaging response generation. (2) exploring data augmentation strategies, such as leveraging gpt-3 to synthesize large-scale training data. ## conclusion in this paper, we introduce tacobot, a modular task-oriented dialogue system that assists users in accomplishing intricate daily tasks. we propose a comprehensive set of modules and approaches to create a collaborative and engaging task bot. to ensure a strong foundation, we employ several data augmentation techniques leveraging llms. furthermore, we open-source the framework and datasets, providing a valuable resource and inspiring future efforts to enhance user-bot collaboration.