Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

NAACL 2019 Tiancheng ZhaoKaige XieMaxine Eskenazi

Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use handcrafted dialog acts, or the output vocabulary, e.g. in neural encoder decoders, as the action spaces... (read more)

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