Papers › Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
Peixiang Zhong, Di Wang, Chunyan Miao
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | DailyDialog | KET | Micro-F1 | 53.37 | #19 of 22 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EC | KET | Micro-F1 | 0.7413 | #8 of 9 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EmoryNLP | KET | Weighted-F1 | 34.39 | #28 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | KET | Micro-F1 | 61.11 | #54 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | KET | Weighted-F1 | 61.33 | #54 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | KET | Weighted-F1 | 58.18 | #62 of 68 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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