Papers › Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion...
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition
Jingye Li, Meishan Zhang, Donghong Ji, Yijiang Liu
Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the utterance representation in conversations. By this method, we can learn better speaker-aware contextual representations from the additional SI corpus. Experiments on two benchmark datasets demonstrate that the proposed architecture is highly effective for CER, obtaining new state-of-the-art results on two datasets.
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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 | EmoryNLP | BERT+MTL | Weighted-F1 | 35.92 | #24 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EmoryNLP | GloVE+MTL | Weighted-F1 | 34.54 | #26 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | BERT+MTL | Weighted-F1 | 61.90 | #50 of 68 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | GloVE+MTL | Weighted-F1 | 60.69 | #55 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.
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