Abstract
Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines. The rise of pre-trained language models (PLMs) has further pushed the limit of ERC performance. However, most recent works on ERC using PLMs are heavily data-driven, and requires fine-tuning the entire PLMs. To improve both sample and computational efficiency, we propose a derivative-free optimization method called Cross-Task Prompt Tuning (CTPT) for few-shot conversational emotion recognition. Unlike existing methods that learn independent knowledge from individual tasks, CTPT leverages sharable cross-task knowledge by exploiting external knowledge from other source tasks to improve learning performance under the few-shot setting. Moreover, CTPT only needs to optimize a vector under the low intrinsic dimensionality without gradient, which is highly parameter-efficient compared with existing approaches. Experiments on five different contextual conversation datasets demonstrate that our CTPT method has superior results on both few-shot scenarios and zero-shot transfers.- Anthology ID:
- 2023.findings-emnlp.780
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11654–11666
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.780
- DOI:
- 10.18653/v1/2023.findings-emnlp.780
- Cite (ACL):
- Yige Xu, Zhiwei Zeng, and Zhiqi Shen. 2023. Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11654–11666, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition (Xu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.780.pdf