SCIENCE@home

Adversarial Learning for Neural Dialogue Generation

Jiwei Li,Will Monroe,Tianlin Shi,Sébastien Jean,Alan Ritter,Dan Jurafsky

Published 2017 in Conference on Empirical Methods in Natural Language Processing

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 machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-52 of 52 references · Page 1 of 1

CITED BY

Showing 1-100 of 917 citing papers · Page 1 of 10

123410
Next