Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection
Jingyao Tang, Lishuang Li, Hongbin Lu, Xueyang Qin, Beibei Zhang, Haiming Wu
Abstract
Few-shot Event Detection (FSED) is a meaningful task due to the limited labeled data and expensive manual labeling. Some prompt-based methods are used in FSED. However, these methods require large GPU memory due to the increased length of input tokens caused by concatenating prompts, as well as additional human effort for designing verbalizers. Moreover, they ignore instance and prompt biases arising from the confounding effects between prompts and texts. In this paper, we propose a prototype-based prompt-instance Interaction with causal Intervention (2xInter) model to conveniently utilize both prompts and verbalizers and effectively eliminate all biases. Specifically, 2xInter first presents a Prototype-based Prompt-Instance Interaction (PPII) module that applies an interactive approach for texts and prompts to reduce memory and regards class prototypes as verbalizers to avoid design costs. Next, 2xInter constructs a Structural Causal Model (SCM) to explain instance and prompt biases and designs a Double-View Causal Intervention (DVCI) module to eliminate these biases. Due to limited supervised information, DVCI devises a generation-based prompt adjustment for instance intervention and a Siamese network-based instance contrasting for prompt intervention. Finally, the experimental results show that 2xInter achieves state-of-the-art performance on RAMS and ACE datasets.- Anthology ID:
- 2024.lrec-main.1161
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 13269–13278
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1161
- DOI:
- Cite (ACL):
- Jingyao Tang, Lishuang Li, Hongbin Lu, Xueyang Qin, Beibei Zhang, and Haiming Wu. 2024. Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13269–13278, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (Tang et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1161.pdf