@inproceedings{liu-jaidka-2023-psyam,
title = "{I} am {P}sy{AM}: Modeling Happiness with Cognitive Appraisal Dimensions",
author = "Liu, Xuan and
Jaidka, Kokil",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.77",
doi = "10.18653/v1/2023.findings-acl.77",
pages = "1192--1210",
abstract = "This paper proposes and evaluates PsyAM (\url{https://anonymous.4open.science/r/BERT-PsyAM-10B9}), a framework that incorporates adaptor modules in a sequential multi-task learning setup to generate high-dimensional feature representations of hedonic well-being (momentary happiness) in terms of its psychological underpinnings. PsyAM models emotion in text through its cognitive antecedents through auxiliary models that achieve multi-task learning through novel feature fusion methods. We show that BERT-PsyAM has cross-task validity and cross-domain generalizability through experiments with emotion-related tasks {--} on new emotion tasks and new datasets, as well as against traditional methods and BERT baselines. We further probe the robustness of BERT-PsyAM through feature ablation studies, as well as discuss the qualitative inferences we can draw regarding the effectiveness of the framework for representing emotional states. We close with a discussion of a future agenda of psychology-inspired neural network architectures.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liu-jaidka-2023-psyam">
<titleInfo>
<title>I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kokil</namePart>
<namePart type="family">Jaidka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2023</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="family">Rogers</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jordan</namePart>
<namePart type="family">Boyd-Graber</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Naoaki</namePart>
<namePart type="family">Okazaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper proposes and evaluates PsyAM (https://anonymous.4open.science/r/BERT-PsyAM-10B9), a framework that incorporates adaptor modules in a sequential multi-task learning setup to generate high-dimensional feature representations of hedonic well-being (momentary happiness) in terms of its psychological underpinnings. PsyAM models emotion in text through its cognitive antecedents through auxiliary models that achieve multi-task learning through novel feature fusion methods. We show that BERT-PsyAM has cross-task validity and cross-domain generalizability through experiments with emotion-related tasks – on new emotion tasks and new datasets, as well as against traditional methods and BERT baselines. We further probe the robustness of BERT-PsyAM through feature ablation studies, as well as discuss the qualitative inferences we can draw regarding the effectiveness of the framework for representing emotional states. We close with a discussion of a future agenda of psychology-inspired neural network architectures.</abstract>
<identifier type="citekey">liu-jaidka-2023-psyam</identifier>
<identifier type="doi">10.18653/v1/2023.findings-acl.77</identifier>
<location>
<url>https://aclanthology.org/2023.findings-acl.77</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>1192</start>
<end>1210</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions
%A Liu, Xuan
%A Jaidka, Kokil
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liu-jaidka-2023-psyam
%X This paper proposes and evaluates PsyAM (https://anonymous.4open.science/r/BERT-PsyAM-10B9), a framework that incorporates adaptor modules in a sequential multi-task learning setup to generate high-dimensional feature representations of hedonic well-being (momentary happiness) in terms of its psychological underpinnings. PsyAM models emotion in text through its cognitive antecedents through auxiliary models that achieve multi-task learning through novel feature fusion methods. We show that BERT-PsyAM has cross-task validity and cross-domain generalizability through experiments with emotion-related tasks – on new emotion tasks and new datasets, as well as against traditional methods and BERT baselines. We further probe the robustness of BERT-PsyAM through feature ablation studies, as well as discuss the qualitative inferences we can draw regarding the effectiveness of the framework for representing emotional states. We close with a discussion of a future agenda of psychology-inspired neural network architectures.
%R 10.18653/v1/2023.findings-acl.77
%U https://aclanthology.org/2023.findings-acl.77
%U https://doi.org/10.18653/v1/2023.findings-acl.77
%P 1192-1210
Markdown (Informal)
[I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions](https://aclanthology.org/2023.findings-acl.77) (Liu & Jaidka, Findings 2023)
ACL