@inproceedings{soumitra-etal-2023-leveraging,
title = "Leveraging Empathy, Distress, and Emotion for Accurate Personality Subtyping from Complex Human Textual Responses",
author = "Soumitra, Ghosh and
Tanisha, Tiwari and
Chetna, Painkra and
Gopendra Vikram, Singh and
Asif, Ekbal",
editor = "Jyoti, D. Pawar and
Sobha, Lalitha Devi",
booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2023",
address = "Goa University, Goa, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2023.icon-1.68",
pages = "679--695",
abstract = "Automated personality subtyping is a crucial area of research with diverse applications in psychology, healthcare, and marketing. However, current studies face challenges such as insufficient data, noisy text data, and difficulty in capturing complex personality traits. To address these issues, including empathy, distress, and emotion as auxiliary tasks in automated personality subtyping may enhance accuracy and robustness. This study introduces a Multi-input Multi-task Framework for Personality, Empathy, Distress, and Emotion Detection (MultiPEDE). This framework harnesses the complementary information from empathy, distress, and emotion tasks (auxiliary tasks) to enhance the accuracy and generalizability of automated personality subtyping (the primary task). The model uses a novel deep-learning architecture that captures the interdependencies between these constructs, is end-to-end trainable, and does not rely on ensemble strategies, making it practical for real-world applications. Performance evaluation involves labeled examples of five personality traits, two classes each for personality, empathy, and distress detection, and seven classes for emotion detection. This approach has diverse applications, including mental health diagnosis, improving online services, and aiding job candidate selection.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="soumitra-etal-2023-leveraging">
<titleInfo>
<title>Leveraging Empathy, Distress, and Emotion for Accurate Personality Subtyping from Complex Human Textual Responses</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ghosh</namePart>
<namePart type="family">Soumitra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tiwari</namePart>
<namePart type="family">Tanisha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Painkra</namePart>
<namePart type="family">Chetna</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Singh</namePart>
<namePart type="family">Gopendra Vikram</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekbal</namePart>
<namePart type="family">Asif</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
</titleInfo>
<name type="personal">
<namePart type="given">D</namePart>
<namePart type="given">Pawar</namePart>
<namePart type="family">Jyoti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lalitha</namePart>
<namePart type="given">Devi</namePart>
<namePart type="family">Sobha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>NLP Association of India (NLPAI)</publisher>
<place>
<placeTerm type="text">Goa University, Goa, India</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Automated personality subtyping is a crucial area of research with diverse applications in psychology, healthcare, and marketing. However, current studies face challenges such as insufficient data, noisy text data, and difficulty in capturing complex personality traits. To address these issues, including empathy, distress, and emotion as auxiliary tasks in automated personality subtyping may enhance accuracy and robustness. This study introduces a Multi-input Multi-task Framework for Personality, Empathy, Distress, and Emotion Detection (MultiPEDE). This framework harnesses the complementary information from empathy, distress, and emotion tasks (auxiliary tasks) to enhance the accuracy and generalizability of automated personality subtyping (the primary task). The model uses a novel deep-learning architecture that captures the interdependencies between these constructs, is end-to-end trainable, and does not rely on ensemble strategies, making it practical for real-world applications. Performance evaluation involves labeled examples of five personality traits, two classes each for personality, empathy, and distress detection, and seven classes for emotion detection. This approach has diverse applications, including mental health diagnosis, improving online services, and aiding job candidate selection.</abstract>
<identifier type="citekey">soumitra-etal-2023-leveraging</identifier>
<location>
<url>https://aclanthology.org/2023.icon-1.68</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>679</start>
<end>695</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Leveraging Empathy, Distress, and Emotion for Accurate Personality Subtyping from Complex Human Textual Responses
%A Soumitra, Ghosh
%A Tanisha, Tiwari
%A Chetna, Painkra
%A Gopendra Vikram, Singh
%A Asif, Ekbal
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F soumitra-etal-2023-leveraging
%X Automated personality subtyping is a crucial area of research with diverse applications in psychology, healthcare, and marketing. However, current studies face challenges such as insufficient data, noisy text data, and difficulty in capturing complex personality traits. To address these issues, including empathy, distress, and emotion as auxiliary tasks in automated personality subtyping may enhance accuracy and robustness. This study introduces a Multi-input Multi-task Framework for Personality, Empathy, Distress, and Emotion Detection (MultiPEDE). This framework harnesses the complementary information from empathy, distress, and emotion tasks (auxiliary tasks) to enhance the accuracy and generalizability of automated personality subtyping (the primary task). The model uses a novel deep-learning architecture that captures the interdependencies between these constructs, is end-to-end trainable, and does not rely on ensemble strategies, making it practical for real-world applications. Performance evaluation involves labeled examples of five personality traits, two classes each for personality, empathy, and distress detection, and seven classes for emotion detection. This approach has diverse applications, including mental health diagnosis, improving online services, and aiding job candidate selection.
%U https://aclanthology.org/2023.icon-1.68
%P 679-695
Markdown (Informal)
[Leveraging Empathy, Distress, and Emotion for Accurate Personality Subtyping from Complex Human Textual Responses](https://aclanthology.org/2023.icon-1.68) (Soumitra et al., ICON 2023)
ACL
- Ghosh Soumitra, Tiwari Tanisha, Painkra Chetna, Singh Gopendra Vikram, and Ekbal Asif. 2023. Leveraging Empathy, Distress, and Emotion for Accurate Personality Subtyping from Complex Human Textual Responses. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 679–695, Goa University, Goa, India. NLP Association of India (NLPAI).