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Retrieval-enhanced Adversarial Training For Neural Response Generation

Qingfu Zhu, Lei Cui, Weinan Zhang, Furu Wei, Ting Liu . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 45 citations

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ACL Datasets Dialogue & Multi Turn Evaluation Interdisciplinary Approaches Security Tools Training Techniques

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.

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