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

Lingbo Mo, Shijie Chen, Ziru Chen, Xiang Deng, Ashley Lewis +8 more
7/29/2023
cs.CL

No official code found. Best implementation: OSU-NLP-Group/TacoBot (3 stars, MIT, last commit 2024-11-22).

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.

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Code Implementations(1)

OSU-NLP-Group/TacoBotCommunity100%
MIT
31Shell, PythonJul 28, 20231 years agoMIT

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Cite this paper

@article{mo2023roll,
  title  = {Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System},
  author = {Lingbo Mo and Shijie Chen and Ziru Chen and Xiang Deng and Ashley Lewis and Sunit Singh and Samuel Stevens and Chang-You Tai and Zhen Wang and Xiang Yue and Tianshu Zhang and Yu Su and Huan Sun},
  year   = {2023},
  eprint = {2307.16081},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.16081v1}
}

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