Abstract
Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories. The existing studies working on emotion detection usually focus on how to improve the performance of model prediction, in which emotions are represented with one-hot vectors. However, emotion relations are ignored in one-hot representations. In this article, we first propose a general framework to learn the distributed representations for emotion categories in emotion space from a given emotion classification dataset. Furthermore, based on the soft labels predicted by the pre-trained neural network model, we derive a simple and effective algorithm. Experiments have validated that the proposed representations in emotion space can express emotion relations much better than word vectors in semantic space.- Anthology ID:
- 2021.acl-long.184
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2364–2375
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.184
- DOI:
- 10.18653/v1/2021.acl-long.184
- Cite (ACL):
- Xiangyu Wang and Chengqing Zong. 2021. Distributed Representations of Emotion Categories in Emotion Space. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2364–2375, Online. Association for Computational Linguistics.
- Cite (Informal):
- Distributed Representations of Emotion Categories in Emotion Space (Wang & Zong, ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.acl-long.184.pdf
- Data
- GoEmotions, ISEAR