  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.
Experiments on multiple textual conversation datasets demonstrate that both
context and commonsense knowledge are consistently beneficial to the emotion
detection performance. In addition, the experimental results show that our KET
model outperforms the state-of-the-art models on most of the tested datasets in
F1 score.
