Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, Pascale Fung
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- github.com/pxuab/emo2vec_wassa_paperOfficialIn paperpytorch★ 0
Abstract
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors. We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection, and personality recognition. Our evaluation of Emo2Vec shows that it outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora. When concatenated with GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on several tasks using a simple logistic regression classifier.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| SST-2 Binary classification | GloVe+Emo2Vec | Accuracy | 82.3 | — | Unverified |
| SST-2 Binary classification | Emo2Vec | Accuracy | 81.2 | — | Unverified |
| SST-5 Fine-grained classification | GloVe+Emo2Vec | Accuracy | 43.6 | — | Unverified |
| SST-5 Fine-grained classification | Emo2Vec | Accuracy | 41.6 | — | Unverified |