  Emotion dynamics modeling is a significant task in emotion recognition in
conversation. It aims to predict conversational emotions when building
empathetic dialogue systems. Existing studies mainly develop models based on
Recurrent Neural Networks (RNNs). They cannot benefit from the power of the
recently-developed pre-training strategies for better token representation
learning in conversations. More seriously, it is hard to distinguish the
dependency of interlocutors and the emotional influence among interlocutors by
simply assembling the features on top of RNNs. In this paper, we develop a
series of BERT-based models to specifically capture the inter-interlocutor and
intra-interlocutor dependencies of the conversational emotion dynamics.
Concretely, we first substitute BERT for RNNs to enrich the token
representations. Then, a Flat-structured BERT (F-BERT) is applied to link up
utterances in a conversation directly, and a Hierarchically-structured BERT
(H-BERT) is employed to distinguish the interlocutors when linking up
utterances. More importantly, a Spatial-Temporal-structured BERT, namely
ST-BERT, is proposed to further determine the emotional influence among
interlocutors. Finally, we conduct extensive experiments on two popular emotion
recognition in conversation benchmark datasets and demonstrate that our
proposed models can attain around 5\% and 10\% improvement over the
state-of-the-art baselines, respectively.
