Abstract
Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the
advantages of different models. In this paper, we propose a Retrieval-Enhanced Adversarial Training (REAT) method for neural
response generation. Distinct from existing
approaches, the REAT method leverages an
encoder-decoder framework in terms of an adversarial training paradigm, while taking advantage of N-best response candidates from
a retrieval-based system to construct the discriminator. An empirical study on a large scale
public available benchmark dataset shows that
the REAT method significantly outperforms
the vanilla Seq2Seq model as well as the conventional adversarial training approach