SpanEmo: Casting Multi-label Emotion Classification as Span-prediction (2021.eacl-main)
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| Challenge: | Current approaches to ER ignore potential ambiguities, in which multiple emotions overlap. |
| Approach: | They propose a model "SpanEmo" which casts multi-label emotion classification as span-prediction and introduces a loss function focused on modelling multiple co-existing emotions in a sentence. |
| Outcome: | The proposed model can predict multiple co-existing emotions in a sentence and improve model performance and learning meaningful associations between labels and words in the sentence. |
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| Challenge: | Existing methods for multi-label emotion classification are based on binary relevance and classifier chain (CC) |
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| Challenge: | Existing methods for predicting how events induce emotions ignore the semantics of the labels themselves. |
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| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
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SpanNER: Named Entity Re-/Recognition as Span Prediction (2021.acl-long)
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| Challenge: | Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. |
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| Challenge: | Emotions manifest through physical experiences and bodily reactions, yet identifying such embodied emotions in text remains understudied. |
| Approach: | They propose to extend existing binary embodied emotion dataset with Ekman’s six basic emotion categories. |
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| Challenge: | Existing approaches to emotion detection are lexicon-based, graphical model-based and linear classifier-based. |
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Distributed Representations of Emotion Categories in Emotion Space (2021.acl-long)
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| Challenge: | Existing studies on emotion detection focus on how to improve performance of models . however, emotion relations are ignored in one-hot representations . |
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| Challenge: | Emotion Representation Mapping (ERM) is an alternative to Word Emotion Induction (WEI) for automatic emotion lexicon construction. |
| Approach: | They propose a neural network approach to ERM that converts existing emotion ratings from one representation format into another by mapping Valence-Arousal-Dominance annotations into Ekman’s Basic Emotions. |
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MultiEMO: An Attention-Based Correlation-Aware Multimodal Fusion Framework for Emotion Recognition in Conversations (2023.acl-long)
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| Challenge: | Emotion Recognition in Conversations (ERC) is an increasingly popular task in the field of Natural Language Processing. |
| Approach: | They propose a framework that captures cross-modal mapping relationships across modalities . they propose 'multiemotion-aware' framework that integrates multimodal cues into the model . |
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