@inproceedings{tang-etal-2024-prototype-based,
title = "Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection",
author = "Tang, Jingyao and
Li, Lishuang and
Lu, Hongbin and
Qin, Xueyang and
Zhang, Beibei and
Wu, Haiming",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1161",
pages = "13269--13278",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection
%A Tang, Jingyao
%A Li, Lishuang
%A Lu, Hongbin
%A Qin, Xueyang
%A Zhang, Beibei
%A Wu, Haiming
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tang-etal-2024-prototype-based
%X 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.
%U https://aclanthology.org/2024.lrec-main.1161
%P 13269-13278
Markdown (Informal)
[Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection](https://aclanthology.org/2024.lrec-main.1161) (Tang et al., LREC-COLING 2024)
ACL