  Emotion Recognition in Conversation (ERC) is a more challenging task than
conventional text emotion recognition. It can be regarded as a personalized and
interactive emotion recognition task, which is supposed to consider not only
the semantic information of text but also the influences from speakers. The
current method models speakers' interactions by building a relation between
every two speakers. However, this fine-grained but complicated modeling is
computationally expensive, hard to extend, and can only consider local context.
To address this problem, we simplify the complicated modeling to a binary
version: Intra-Speaker and Inter-Speaker dependencies, without identifying
every unique speaker for the targeted speaker. To better achieve the simplified
interaction modeling of speakers in Transformer, which shows excellent ability
to settle long-distance dependency, we design three types of masks and
respectively utilize them in three independent Transformer blocks. The designed
masks respectively model the conventional context modeling, Intra-Speaker
dependency, and Inter-Speaker dependency. Furthermore, different speaker-aware
information extracted by Transformer blocks diversely contributes to the
prediction, and therefore we utilize the attention mechanism to automatically
weight them. Experiments on two ERC datasets indicate that our model is
efficacious to achieve better performance.
