Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns a meaningful representation of sentences by autoencoding and then learns to map an input query to the response representation, which is in turn decoded as a response sentence. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant, and diverse responses than existing state-of-the-art methods.
@article{arxiv.1911.03817,
title = {Adversarial Learning on the Latent Space for Diverse Dialog Generation},
author = {Kashif Khan and Gaurav Sahu and Vikash Balasubramanian and Lili Mou and Olga Vechtomova},
journal= {arXiv preprint arXiv:1911.03817},
year = {2020}
}