Abstract In this paper, we address cross-domain multi-dimensional emotion regression through adversarial learning. This is done via a proper conduction of both dimensional and domanial adversarial learning. On the one hand, we conduct adversarial learning between emotion dimensions via a dimensional discriminator to achieve dimension-specific features through the attention mechanism for better determining dimensional emotion scores. On the other hand, we conduct adversarial learning between the target domain and multiple source domains via a domanial discriminator for better leveraging texts from source domains for regression model training in the target domain. Empirical evaluation on the EMOBANK corpus shows that our proposed approach achieves notable improvements in r-values in the cross-domain multi-dimensional emotion regression task over the state-of-the-art baselines.

中文翻译：

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用于情感回归的领域和维度对抗学习

摘要 在本文中，我们通过对抗性学习解决跨域多维情感回归问题。这是通过维度和领域对抗学习的适当传导来完成的。一方面，我们通过维度鉴别器在情感维度之间进行对抗学习，通过注意力机制实现维度特定的特征，从而更好地确定维度情感分数。另一方面，我们通过域鉴别器在目标域和多个源域之间进行对抗性学习，以便更好地利用源域中的文本在目标域中进行回归模型训练。