  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.
