Abstract
One-hot labels are commonly employed as ground truth in Emotion Recognition in Conversations (ERC). However, this approach may not fully encompass all the emotions conveyed in a single utterance, leading to suboptimal performance. Regrettably, current ERC datasets lack comprehensive emotionally distributed labels. To address this issue, we propose the Emotion Label Refinement (EmoLR) method, which utilizes context- and speaker-sensitive information to infer mixed emotional labels. EmoLR comprises an Emotion Predictor (EP) module and a Label Refinement (LR) module. The EP module recognizes emotions and provides context/speaker states for the LR module. Subsequently, the LR module calculates the similarity between these states and ground-truth labels, generating a refined label distribution (RLD). The RLD captures a more comprehensive range of emotions than the original one-hot labels. These refined labels are then used for model training in place of the one-hot labels. Experimental results on three public conversational datasets demonstrate that our EmoLR achieves state-of-the-art performance.- Anthology ID:
- 2023.tacl-1.84
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 11
- Month:
- Year:
- 2023
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1485–1499
- Language:
- URL:
- https://aclanthology.org/2023.tacl-1.84
- DOI:
- 10.1162/tacl_a_00614
- Cite (ACL):
- Jintao Wen, Geng Tu, Rui Li, Dazhi Jiang, and Wenhua Zhu. 2023. Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations. Transactions of the Association for Computational Linguistics, 11:1485–1499.
- Cite (Informal):
- Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations (Wen et al., TACL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.tacl-1.84.pdf