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Enhancing Cognitive Models of Emotions with Representation Learning ...
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Abstract:
We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions that can be used to computationally describe psychological models of emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model that enables to interpret dynamically learned representations optimized for an emotion classification task. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions. Our layer analysis can derive an emotion graph to depict hierarchical relations among the emotions. Our emotion representations can be used to generate an emotion wheel directly comparable to the one from Plutchik's\LN model, and also augment the values of missing emotions in the PAD emotional state model. ... : Accepted by the NAACL Workshop on Cognitive Modeling and Computational Linguistics 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2104.10117 https://arxiv.org/abs/2104.10117
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Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation ...
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Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope ...
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The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders ...
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Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph ...
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Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering ...
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Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings ...
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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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20 |
Universal Dependencies 2.1
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In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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