@inproceedings{tiwari-etal-2022-iaemp,
title = "{IAE}mp: Intent-aware Empathetic Response Generation",
author = "Tiwari, Mrigank and
Dahiya, Vivek and
Mohanty, Om and
Saride, Girija",
editor = "Akhtar, Md. Shad and
Chakraborty, Tanmoy",
booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2022",
address = "New Delhi, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.icon-main.7",
pages = "55--59",
abstract = "In the domain of virtual assistants or conversational systems, it is important to empathise with the user. Being empathetic involves understanding the emotion of the ongoing dialogue and responding to the situation with empathy. We propose a novel approach for empathetic response generation, which leverages predicted intents for future response and prompts the encoder-decoder model to improve empathy in generated responses. Our model exploits the combination of dialogues and their respective emotions to generate empathetic response. As responding intent plays an important part in our generation, we also employ one or more intents to generate responses with relevant empathy. We achieve improved human and automated metrics, compared to the baselines.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tiwari-etal-2022-iaemp">
<titleInfo>
<title>IAEmp: Intent-aware Empathetic Response Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mrigank</namePart>
<namePart type="family">Tiwari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivek</namePart>
<namePart type="family">Dahiya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Om</namePart>
<namePart type="family">Mohanty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Girija</namePart>
<namePart type="family">Saride</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th International Conference on Natural Language Processing (ICON)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="given">Shad</namePart>
<namePart type="family">Akhtar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Delhi, India</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In the domain of virtual assistants or conversational systems, it is important to empathise with the user. Being empathetic involves understanding the emotion of the ongoing dialogue and responding to the situation with empathy. We propose a novel approach for empathetic response generation, which leverages predicted intents for future response and prompts the encoder-decoder model to improve empathy in generated responses. Our model exploits the combination of dialogues and their respective emotions to generate empathetic response. As responding intent plays an important part in our generation, we also employ one or more intents to generate responses with relevant empathy. We achieve improved human and automated metrics, compared to the baselines.</abstract>
<identifier type="citekey">tiwari-etal-2022-iaemp</identifier>
<location>
<url>https://aclanthology.org/2022.icon-main.7</url>
</location>
<part>
<date>2022-12</date>
<extent unit="page">
<start>55</start>
<end>59</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T IAEmp: Intent-aware Empathetic Response Generation
%A Tiwari, Mrigank
%A Dahiya, Vivek
%A Mohanty, Om
%A Saride, Girija
%Y Akhtar, Md. Shad
%Y Chakraborty, Tanmoy
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON)
%D 2022
%8 December
%I Association for Computational Linguistics
%C New Delhi, India
%F tiwari-etal-2022-iaemp
%X In the domain of virtual assistants or conversational systems, it is important to empathise with the user. Being empathetic involves understanding the emotion of the ongoing dialogue and responding to the situation with empathy. We propose a novel approach for empathetic response generation, which leverages predicted intents for future response and prompts the encoder-decoder model to improve empathy in generated responses. Our model exploits the combination of dialogues and their respective emotions to generate empathetic response. As responding intent plays an important part in our generation, we also employ one or more intents to generate responses with relevant empathy. We achieve improved human and automated metrics, compared to the baselines.
%U https://aclanthology.org/2022.icon-main.7
%P 55-59
Markdown (Informal)
[IAEmp: Intent-aware Empathetic Response Generation](https://aclanthology.org/2022.icon-main.7) (Tiwari et al., ICON 2022)
ACL
- Mrigank Tiwari, Vivek Dahiya, Om Mohanty, and Girija Saride. 2022. IAEmp: Intent-aware Empathetic Response Generation. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 55–59, New Delhi, India. Association for Computational Linguistics.