  Emotion recognition in conversation (ERC) has attracted much attention in
recent years for its necessity in widespread applications. Existing ERC methods
mostly model the self and inter-speaker context separately, posing a major
issue for lacking enough interaction between them. In this paper, we propose a
novel Speaker and Position-Aware Graph neural network model for ERC (S+PAGE),
which contains three stages to combine the benefits of both Transformer and
relational graph convolution network (R-GCN) for better contextual modeling.
Firstly, a two-stream conversational Transformer is presented to extract the
coarse self and inter-speaker contextual features for each utterance. Then, a
speaker and position-aware conversation graph is constructed, and we propose an
enhanced R-GCN model, called PAG, to refine the coarse features guided by a
relative positional encoding. Finally, both of the features from the former two
stages are input into a conditional random field layer to model the emotion
transfer.
