Dialogue generation with GAN

Hui Su; Xiaoyu Shen; Pengwei Hu; Wenjie Li; Yun Chen

Dialogue generation with GAN

Hui Su, Xiaoyu Shen, Pengwei Hu, Wenjie Li, Yun Chen

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

13 Citations (Scopus)

Abstract

This paper presents a Generative Adversarial Network (GAN) to model multi-turn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.

Original language	English
Title of host publication	32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Publisher	AAAI press
Pages	8163-8164
Number of pages	2
ISBN (Electronic)	9781577358008
Publication status	Published - 2018
Event	32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States Duration: 2 Feb 2018 → 7 Feb 2018

Publication series

Name	32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Conference

Conference	32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/Territory	United States
City	New Orleans
Period	2/02/18 → 7/02/18

ASJC Scopus subject areas

Artificial Intelligence

Cite this

@inproceedings{997f4a92b0bc417dbbb7bd002154cc3d,

title = "Dialogue generation with GAN",

abstract = "This paper presents a Generative Adversarial Network (GAN) to model multi-turn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.",

author = "Hui Su and Xiaoyu Shen and Pengwei Hu and Wenjie Li and Yun Chen",

note = "Publisher Copyright: Copyright {\textcopyright} 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 ; Conference date: 02-02-2018 Through 07-02-2018",

year = "2018",

language = "English",

series = "32nd AAAI Conference on Artificial Intelligence, AAAI 2018",

publisher = "AAAI press",

pages = "8163--8164",

booktitle = "32nd AAAI Conference on Artificial Intelligence, AAAI 2018",

}

Dialogue generation with GAN. / Su, Hui; Shen, Xiaoyu; Hu, Pengwei et al.
32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 8163-8164 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Dialogue generation with GAN

AU - Su, Hui

AU - Shen, Xiaoyu

AU - Hu, Pengwei

AU - Li, Wenjie

AU - Chen, Yun

PY - 2018

Y1 - 2018

N2 - This paper presents a Generative Adversarial Network (GAN) to model multi-turn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.

AB - This paper presents a Generative Adversarial Network (GAN) to model multi-turn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.

UR - http://www.scopus.com/inward/record.url?scp=85060495106&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

AN - SCOPUS:85060495106

T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018

SP - 8163

EP - 8164

BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018

PB - AAAI press

T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Y2 - 2 February 2018 through 7 February 2018

ER -

Dialogue generation with GAN

Abstract

Publication series

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this