Adversarial Learning for Neural Dialogue Generation
Published: Jan 1, 2017
Abstract
We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the...
Paper Details
Title
Adversarial Learning for Neural Dialogue Generation
Published Date
Jan 1, 2017
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