Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Understand

Messages in human conversations inherently convey emotions.

• The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks.
• However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions.
• In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism.