We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FloDial) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FloNet, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FloNet can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.

本文提出了一种在端到端的任务导向对话（TOD）学习中的新问题，其中对话系统模拟故障排除代理人，帮助用户诊断问题。我们发布了一个数据集（FloDial）并设计了一个神经模型（FloNet），实验证明FloNet可以进行零-shot迁移到未见过的流程图，并为未来研究设置了强有力的基准线。

基于流程图的任务导向对话的端到端学习