This paper concentrates on the understanding of interlocutors' emotions
evoked in conversational utterances. Previous studies in this literature mainly
focus on more accurate emotional predictions, while ignoring model robustness
when the local context is corrupted by adversarial attacks. To maintain
robustness while ensuring accuracy, we propose an emotion recognizer augmented
by a full-attention topic regularizer, which enables an emotion-related global
view when modeling the local context in a conversation. A joint topic modeling
strategy is introduced to implement regularization from both representation and
loss perspectives. To avoid over-regularization, we drop the constraints on
prior distributions that exist in traditional topic modeling and perform
probabilistic approximations based entirely on attention alignment. Experiments
show that our models obtain more favorable results than state-of-the-art
models, and gain convincing robustness under three types of adversarial
attacks.

提出了一种情感识别器，利用全注意力主题正则化器将情感相关的全局视角引入到会话的局部上下文建模中，从表示和损失的角度实现正则化，通过注意力对齐实现对传统主题建模上的概率近似，并在三种类型的对抗攻击下取得更好的结果和令人信服的鲁棒性。