Retrieval-Enhanced Adversarial Training for Neural Response Generation

Qingfu Zhu, Lei Cui, Wei-Nan Zhang, Furu Wei, Ting Liu


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.
Anthology ID:
P19-1366
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3763–3773
URL:
https://www.aclweb.org/anthology/P19-1366
DOI:
10.18653/v1/P19-1366
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PDF:
https://www.aclweb.org/anthology/P19-1366.pdf