Adversarial Learning on the Latent Space for Diverse Dialog Generation
Full Text: 2020.coling-main.441.pdf
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.
Citation
K. Khan, G. Sahu, V. Balasubramanian, L. Mou, O. Vechtomova. "Adversarial Learning on the Latent Space for Diverse Dialog Generation". Conference on Computational Linguistics (COLING), pp 5026-5034, December 2020.| Keywords: | |
| Category: | In Conference |
| Web Links: | doi |
| ACL |
BibTeX
@incollection{Khan+al:COLING20,
author = {Kashif Khan and Gaurav Sahu and Vikash Balasubramanian and Lili Mou
and Olga Vechtomova},
title = {Adversarial Learning on the Latent Space for Diverse Dialog
Generation},
Pages = {5026-5034},
booktitle = {Conference on Computational Linguistics (COLING)},
year = 2020,
}Last Updated: March 01, 2021Submitted by Sabina P
