Towards Label-Agnostic Emotion Embeddings

Sven Buechel, Luise Modersohn, Udo Hahn


Abstract
Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under-resourced) natural languages and text genres (e.g., product reviews, tweets, news). The resulting heterogeneity makes data and software developed under these conflicting constraints hard to compare and challenging to integrate. To resolve this unsatisfactory state of affairs we here propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. Experiments on a wide range of datasets indicate that this approach yields the desired interoperability without penalizing prediction quality. Code and data are archived under DOI 10.5281/zenodo.5466068.
Anthology ID:
2021.emnlp-main.728
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9231–9249
Language:
URL:
https://aclanthology.org/2021.emnlp-main.728
DOI:
10.18653/v1/2021.emnlp-main.728
Bibkey:
Cite (ACL):
Sven Buechel, Luise Modersohn, and Udo Hahn. 2021. Towards Label-Agnostic Emotion Embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9231–9249, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Towards Label-Agnostic Emotion Embeddings (Buechel et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.728.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.728.mp4