Adversarial Attention Modeling for Multi-dimensional Emotion Regression

Suyang Zhu, Shoushan Li, Guodong Zhou


Abstract
In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.
Anthology ID:
P19-1045
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
471–480
Language:
URL:
https://aclanthology.org/P19-1045
DOI:
10.18653/v1/P19-1045
Bibkey:
Cite (ACL):
Suyang Zhu, Shoushan Li, and Guodong Zhou. 2019. Adversarial Attention Modeling for Multi-dimensional Emotion Regression. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 471–480, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Adversarial Attention Modeling for Multi-dimensional Emotion Regression (Zhu et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1045.pdf