@inproceedings{samad-etal-2022-empathetic,
title = "Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems",
author = "Samad, Azlaan Mustafa and
Mishra, Kshitij and
Firdaus, Mauajama and
Ekbal, Asif",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.63",
doi = "10.18653/v1/2022.findings-naacl.63",
pages = "844--856",
abstract = "Persuasion is an intricate process involving empathetic connection between two individuals. Plain persuasive responses may make a conversation non-engaging. Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener. In this paper, we propose a novel task of incorporating empathy when generating persuasive responses. We develop an empathetic persuasive dialogue system by fine-tuning a maximum likelihood Estimation (MLE)-based language model in a reinforcement learning (RL) framework. To design feedbacks for our RL-agent, we define an effective and efficient reward function considering consistency, repetitiveness, emotion and persuasion rewards to ensure consistency, non-repetitiveness, empathy and persuasiveness in the generated responses. Due to lack of emotion annotated persuasive data, we first annotate the existing Persuaion For Good dataset with emotions, then build transformer based classifiers to provide emotion based feedbacks to our RL agent. Experimental results confirm that our proposed model increases the rate of generating persuasive responses as compared to the available state-of-the-art dialogue models while making the dialogues empathetically more engaging and retaining the language quality in responses.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="samad-etal-2022-empathetic">
<titleInfo>
<title>Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems</title>
</titleInfo>
<name type="personal">
<namePart type="given">Azlaan</namePart>
<namePart type="given">Mustafa</namePart>
<namePart type="family">Samad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kshitij</namePart>
<namePart type="family">Mishra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mauajama</namePart>
<namePart type="family">Firdaus</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Asif</namePart>
<namePart type="family">Ekbal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: NAACL 2022</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marine</namePart>
<namePart type="family">Carpuat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marie-Catherine</namePart>
<namePart type="family">de Marneffe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="given">Vladimir</namePart>
<namePart type="family">Meza Ruiz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Seattle, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Persuasion is an intricate process involving empathetic connection between two individuals. Plain persuasive responses may make a conversation non-engaging. Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener. In this paper, we propose a novel task of incorporating empathy when generating persuasive responses. We develop an empathetic persuasive dialogue system by fine-tuning a maximum likelihood Estimation (MLE)-based language model in a reinforcement learning (RL) framework. To design feedbacks for our RL-agent, we define an effective and efficient reward function considering consistency, repetitiveness, emotion and persuasion rewards to ensure consistency, non-repetitiveness, empathy and persuasiveness in the generated responses. Due to lack of emotion annotated persuasive data, we first annotate the existing Persuaion For Good dataset with emotions, then build transformer based classifiers to provide emotion based feedbacks to our RL agent. Experimental results confirm that our proposed model increases the rate of generating persuasive responses as compared to the available state-of-the-art dialogue models while making the dialogues empathetically more engaging and retaining the language quality in responses.</abstract>
<identifier type="citekey">samad-etal-2022-empathetic</identifier>
<identifier type="doi">10.18653/v1/2022.findings-naacl.63</identifier>
<location>
<url>https://aclanthology.org/2022.findings-naacl.63</url>
</location>
<part>
<date>2022-07</date>
<extent unit="page">
<start>844</start>
<end>856</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems
%A Samad, Azlaan Mustafa
%A Mishra, Kshitij
%A Firdaus, Mauajama
%A Ekbal, Asif
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F samad-etal-2022-empathetic
%X Persuasion is an intricate process involving empathetic connection between two individuals. Plain persuasive responses may make a conversation non-engaging. Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener. In this paper, we propose a novel task of incorporating empathy when generating persuasive responses. We develop an empathetic persuasive dialogue system by fine-tuning a maximum likelihood Estimation (MLE)-based language model in a reinforcement learning (RL) framework. To design feedbacks for our RL-agent, we define an effective and efficient reward function considering consistency, repetitiveness, emotion and persuasion rewards to ensure consistency, non-repetitiveness, empathy and persuasiveness in the generated responses. Due to lack of emotion annotated persuasive data, we first annotate the existing Persuaion For Good dataset with emotions, then build transformer based classifiers to provide emotion based feedbacks to our RL agent. Experimental results confirm that our proposed model increases the rate of generating persuasive responses as compared to the available state-of-the-art dialogue models while making the dialogues empathetically more engaging and retaining the language quality in responses.
%R 10.18653/v1/2022.findings-naacl.63
%U https://aclanthology.org/2022.findings-naacl.63
%U https://doi.org/10.18653/v1/2022.findings-naacl.63
%P 844-856
Markdown (Informal)
[Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems](https://aclanthology.org/2022.findings-naacl.63) (Samad et al., Findings 2022)
ACL