Adversarial Learning for Neural Dialogue Generation

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-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues.

• NS
  National Science Foundation

  Awards: 1464128, 1514268, 514268, IIS-1464128
• DA
  Defense Advanced Research Projects Agency
• AR
  Army Research Office

  Awards: W911NF-15-1-, DARPA, W911NF, 911NF