###### Abstract  
Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of \"machine recommendation-human review-machine incremental learning\" was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods. event detection; information extraction; human-in-the-loop; incremental learning; few-shot learning; contrastive learning +
Footnote †: journal: _Applied Sciences_  
Class-incremental learning refers to the model learning new event types, as illustrated in Figure 1b; sample-incremental learning involves the arrival of original class samples in batches. This paper focuses on the scenario of class-incremental learning. In practical applications, new events typically emerge gradually, and initially only have a few samples. To enable the model to identify new events early on, class-incremental learning must be performed with limited samples, which presents the challenge of avoiding overfitting while retaining previously learned classes. Furthermore, in traditional class-incremental learning [2; 3; 4; 5], new training data are not obtained through manual review and correction, whereas in the interactive event-detection mode, new training data are derived from the model's incorrect predictions and corrected by humans, including the model's errors and manual correction feedback. Effectively learning from such feedback is a key issue to be addressed in interactive event-detection modes. To address these issues, we propose a class-incremental learning method for interactive event-detection mode. To tackle the challenges of few-shot class-incremental learning, we introduce semantic-boundary smoothing-based contrastive learning, which assigns soft labels and designs contrastive loss to provide the model with richer gradient information, improving its ability to represent new classes. To learn from human feedback, we treat new-class samples as corrected samples, retaining their outputs from the old model, and then select representative and confusable samples to construct a replay set. To mitigate catastrophic forgetting, we employ a hierarchical distillation to preserve the model's previous knowledge. Our contributions can be summarized as follows:  
1. We introduce an interactive event-detection mode: \"Machine recommends event triggers\(\rightarrow\)Manual review and correction\(\rightarrow\)Machine incremental learning\". This model better aligns with the operational mode required for high-precision applications and provides a reference for subsequent research. 2. We designed the ICD method for class-incremental learning in interactive event-detection tasks. This method constructs a replay set based on representative and confusable samples, designs semantic-boundary smoothing-based contrastive learning to improve performance with few samples, and employs hierarchical distillation to preserve existing knowledge. 3.