@inproceedings{cui-etal-2019-dal,
title = "{DAL}: Dual Adversarial Learning for Dialogue Generation",
author = "Cui, Shaobo and
Lian, Rongzhong and
Jiang, Di and
Song, Yuanfeng and
Bao, Siqi and
Jiang, Yong",
editor = "Bosselut, Antoine and
Celikyilmaz, Asli and
Ghazvininejad, Marjan and
Iyer, Srinivasan and
Khandelwal, Urvashi and
Rashkin, Hannah and
Wolf, Thomas",
booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2302/",
doi = "10.18653/v1/W19-2302",
pages = "11--20",
abstract = "In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cui-etal-2019-dal">
<titleInfo>
<title>DAL: Dual Adversarial Learning for Dialogue Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shaobo</namePart>
<namePart type="family">Cui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rongzhong</namePart>
<namePart type="family">Lian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Di</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuanfeng</namePart>
<namePart type="family">Song</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Siqi</namePart>
<namePart type="family">Bao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yong</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Antoine</namePart>
<namePart type="family">Bosselut</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Asli</namePart>
<namePart type="family">Celikyilmaz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marjan</namePart>
<namePart type="family">Ghazvininejad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Srinivasan</namePart>
<namePart type="family">Iyer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Urvashi</namePart>
<namePart type="family">Khandelwal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hannah</namePart>
<namePart type="family">Rashkin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thomas</namePart>
<namePart type="family">Wolf</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Minneapolis, Minnesota</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations.</abstract>
<identifier type="citekey">cui-etal-2019-dal</identifier>
<identifier type="doi">10.18653/v1/W19-2302</identifier>
<location>
<url>https://aclanthology.org/W19-2302/</url>
</location>
<part>
<date>2019-06</date>
<extent unit="page">
<start>11</start>
<end>20</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T DAL: Dual Adversarial Learning for Dialogue Generation
%A Cui, Shaobo
%A Lian, Rongzhong
%A Jiang, Di
%A Song, Yuanfeng
%A Bao, Siqi
%A Jiang, Yong
%Y Bosselut, Antoine
%Y Celikyilmaz, Asli
%Y Ghazvininejad, Marjan
%Y Iyer, Srinivasan
%Y Khandelwal, Urvashi
%Y Rashkin, Hannah
%Y Wolf, Thomas
%S Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cui-etal-2019-dal
%X In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations.
%R 10.18653/v1/W19-2302
%U https://aclanthology.org/W19-2302/
%U https://doi.org/10.18653/v1/W19-2302
%P 11-20
Markdown (Informal)
[DAL: Dual Adversarial Learning for Dialogue Generation](https://aclanthology.org/W19-2302/) (Cui et al., NAACL 2019)
ACL
- Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, and Yong Jiang. 2019. DAL: Dual Adversarial Learning for Dialogue Generation. In Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation, pages 11–20, Minneapolis, Minnesota. Association for Computational Linguistics.