Papers › EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa

EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa

26 Aug 2021arXiv:2108.12009archive 2025-07-28

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

tae898/erc officialmentioned on GitHubpytorch report

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Tasks

Emotion RecognitionEmotion Recognition in Conversation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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Methods

AdamAttentionAttention DropoutBERTDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionRoBERTaSoftmaxWeight DecayWordPiece

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