@inproceedings{xu-etal-2018-emo2vec,
title = "{E}mo2{V}ec: Learning Generalized Emotion Representation by Multi-task Training",
author = "Xu, Peng and
Madotto, Andrea and
Wu, Chien-Sheng and
Park, Ji Ho and
Fung, Pascale",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6243",
doi = "10.18653/v1/W18-6243",
pages = "292--298",
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.",
}
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%0 Conference Proceedings
%T Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
%A Xu, Peng
%A Madotto, Andrea
%A Wu, Chien-Sheng
%A Park, Ji Ho
%A Fung, Pascale
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xu-etal-2018-emo2vec
%X 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.
%R 10.18653/v1/W18-6243
%U https://aclanthology.org/W18-6243
%U https://doi.org/10.18653/v1/W18-6243
%P 292-298
Markdown (Informal)
[Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training](https://aclanthology.org/W18-6243) (Xu et al., WASSA 2018)
ACL