  In dialogue systems, utterances with similar semantics may have distinctive
emotions under different contexts. Therefore, modeling long-range contextual
emotional relationships with speaker dependency plays a crucial part in
dialogue emotion recognition. Meanwhile, distinguishing the different emotion
categories is non-trivial since they usually have semantically similar
sentiments. To this end, we adopt supervised contrastive learning to make
different emotions mutually exclusive to identify similar emotions better.
Meanwhile, we utilize an auxiliary response generation task to enhance the
model's ability of handling context information, thereby forcing the model to
recognize emotions with similar semantics in diverse contexts. To achieve these
objectives, we use the pre-trained encoder-decoder model BART as our backbone
model since it is very suitable for both understanding and generation tasks.
The experiments on four datasets demonstrate that our proposed model obtains
significantly more favorable results than the state-of-the-art model in
dialogue emotion recognition. The ablation study further demonstrates the
effectiveness of supervised contrastive loss and generative loss.
