@inproceedings{zhu2019retrieval-enhanced,
author = {Zhu, Qingfu and Cui, Lei and Zhang, Wei-Nan and Wei, Furu and Liu, Ting},
title = {Retrieval-Enhanced Adversarial Training for Neural Response Generation},
booktitle = {ACL 2019},
year = {2019},
month = {July},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/retrieval-enhanced-adversarial-training-for-neural-response-generation/},
}