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Feb, 2023
面向多领域和多任务对话的少样本结构化策略学习
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues
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Thibault Cordier, Tanguy Urvoy, Fabrice Lefevre, Lina M. Rojas-Barahona
TL;DR
本研究旨在探讨使用结构化政策提高在多领域和多任务环境下的强化学习样本效率。作者在测试不同结构化水平时,发现图形神经网络具有优势,且建议未来的研究应聚焦于连接人类数据、模拟器和自动评估器。
Abstract
reinforcement learning
has been widely adopted to model
dialogue managers
in task-oriented dialogues. However, the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of
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