  In Emotion Recognition in Conversations (ERC), the emotions of target
utterances are closely dependent on their context. Therefore, existing works
train the model to generate the response of the target utterance, which aims to
recognise emotions leveraging contextual information. However, adjacent
response generation ignores long-range dependencies and provides limited
affective information in many cases. In addition, most ERC models learn a
unified distributed representation for each utterance, which lacks
interpretability and robustness. To address these issues, we propose a
VAD-disentangled Variational AutoEncoder (VAD-VAE), which first introduces a
target utterance reconstruction task based on Variational Autoencoder, then
disentangles three affect representations Valence-Arousal-Dominance (VAD) from
the latent space. We also enhance the disentangled representations by
introducing VAD supervision signals from a sentiment lexicon and minimising the
mutual information between VAD distributions. Experiments show that VAD-VAE
outperforms the state-of-the-art model on two datasets. Further analysis proves
the effectiveness of each proposed module and the quality of disentangled VAD
representations. The code is available at
https://github.com/SteveKGYang/VAD-VAE.
