I am PsyAM: Modeling Happiness with Cognitive Appraisal Dimensions

AbstractThis 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.