Adversarial Learning for Neural Dialogue Generation

@inproceedings{Li2017AdversarialLF,
  title={Adversarial Learning for Neural Dialogue Generation},
  author={Jiwei Li and Will Monroe and Tianlin Shi and Alan Ritter and Daniel Jurafsky},
  booktitle={EMNLP},
  year={2017}
}
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) 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… CONTINUE READING
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