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        <title>I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions</title>
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        <namePart type="given">Xuan</namePart>
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    <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>
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%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)

• I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions (Liu & Jaidka, Findings 2023)

ACL

• Xuan Liu and Kokil Jaidka. 2023. I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1192–1210, Toronto, Canada. Association for Computational Linguistics.