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.

本文提出了 RETE 方式，使用检索方法来增强神经响应生成的对抗训练， 与当前方法不同的是，该方法采用编码器 - 解码器框架，在对抗训练范式中利用从检索系统获取的 N 个最佳响应候选构建鉴别器。一个基于大规模公共可用基准数据集的实证研究表明，REAT 方法明显优于普通 Seq2Seq 模型和传统的对抗性训练方法。