Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level (C18-1)
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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. |
| Outcome: | The proposed model outperforms the state-of-the-art in 13 languages and is almost as reliable as human annotations even in cross-lingual settings. |
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
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| Challenge: | Existing word representations based on distributional hypothesis do not provide accurate representations of emotions. |
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| Challenge: | Emotions manifest through physical experiences and bodily reactions, yet identifying such embodied emotions in text remains understudied. |
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
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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: | a novel and manually corrected emotion lexicon is presented for Alsatian dialects . the dialects are used mainly orally and lack a stable and consensual spelling convention . |
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