Papers › EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa
EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa
Taewoon Kim, Piek Vossen
We present EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa, a simple yet expressive scheme of solving the ERC (emotion recognition in conversation) task. By simply prepending speaker names to utterances and inserting separation tokens between the utterances in a dialogue, EmoBERTa can learn intra- and inter- speaker states and context to predict the emotion of a current speaker, in an end-to-end manner. Our experiments show that we reach a new state of the art on the two popular ERC datasets using a basic and straight-forward approach. We've open sourced our code and models at https://github.com/tae898/erc.
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Code
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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 | CPED | EmoBERTa | Accuracy of Sentiment | 48.09 | #9 of 11 | Archive leaderboard | report |
| Emotion Recognition in Conversation | CPED | EmoBERTa | Macro-F1 of Sentiment | 44.60 | #9 of 11 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | EmoBERTa | Weighted-F1 | 68.57 | #27 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | EmoBERTa | Weighted-F1 | 65.61 | #28 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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