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arxiv: 2207.05223 · v2 · pith:YRYJ7RY7 · submitted 2022-07-11 · cs.CL · cs.AI· cs.LG

Bootstrapping a User-Centered Task-Oriented Dialogue System

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 reserved pith:YRYJ7RY7record.jsonopen to challenge →

classification cs.CL cs.AIcs.LG
keywords dialoguetacobotbootstrappingexperiencelanguagesystemtask-orienteduser-centered
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We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks. TacoBot is designed with a user-centered principle and aspires to deliver a collaborative and accessible dialogue experience. Towards that end, it is equipped with accurate language understanding, flexible dialogue management, and engaging response generation. Furthermore, TacoBot is backed by a strong search engine and an automated end-to-end test suite. In bootstrapping the development of TacoBot, we explore a series of data augmentation strategies to train advanced neural language processing models and continuously improve the dialogue experience with collected real conversations. At the end of the semifinals, TacoBot achieved an average rating of 3.55/5.0.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery

    cs.CL 2026-06 unverdicted novelty 7.0

    MEMPROBE is a benchmark for direct recovery of hidden user states from LLM agent memory, showing task success and memory recovery as distinct capabilities with moderate recovery scores around 0.6.