# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 2 Related Work
### Event Detection  
In the early research of event detection, feature engineering [23; 24] was the main development direction. With the rise of deep learning technology, neural-network-based methods have gradually replaced traditional models that rely on manually crafted features. [PERSON] et al. were the first to apply neural networks for joint event extraction [25], which treats the event extraction process as a sequential labeling task and introduces memory vectors to store information. The drawbacks of pipelined frameworks were better addressed.  
[PERSON] et al. proposed the dbRNN model [26], which effectively connects trigger words with their associated arguments via syntactic paths and enhances semantic modeling capabilities. [PERSON] et al. introduced the DAG-GRU model [27] to address the performance differences and stability of neural networks in event detection tasks. The model improves the performance of trigger word classification by combining contextual and syntactic information. [PERSON] et al. proposed the MOGANED model [28], which constructs dependency syntactic graphs and uses attention mechanisms to focus on higher-order syntactic information. They performed multi classification to enhance the model's ability to distinguish key information.  
In recent years, Transformer-based pre-trained models have demonstrated stronger transferability and semantic understanding capabilities in event detection. [PERSON] and [PERSON] proposed the SS-VQ-VAE model [29] to address the high cost of traditional event extraction methods relying on predefined event types. This model uses a pre-trained BERT to obtain candidate triggers and predict their types. It then uses a variational autoencoder as a regularizer to reconstruct the trigger words and alleviate overfitting to labeled types. [PERSON] et al. constructed a Structural Causal Model (SCM) that introduces backdoor adjustment for causal intervention in the context [30], blocking the confounding effects between trigger words, context, and predicted results. This provides new ideas for improving the generalization of event detection models. [PERSON] et al. proposed the CorED model [31], which utilizes the literal semantics of type names to construct dynamic graph structures and employs graph neural networks to aggregate relevant type information, improving representation learning for low resource types. [PERSON] et al. proposed SMGED [32], which designs a multi-layer graph attention network combined with a skip connection mechanism to capture syntactic dependencies of different orders effectively. Bi-directional Long Short-Term Memory (BiLSTM) is introduced to encode contextual information. Its output is fused with that of the graph attention networks to compensate for the information loss caused by external dependency analysis tools in traditional methods. In low-resource event detection tasks, [PERSON] et al. proposed a syntactic-embedding-based model, SynED [33]. It extracts syntactic information using SpaCy and introduces a syntactic gating matrix in the Transformer's self-attention layer to improve the accuracy of trigger word recognition. [PERSON] et al. proposed the DE3 TC model [34], which concatenates event type clues, type-specific clues, original sentences, and similar type descriptions into a semantic modeling sequence. This sequence is then encoded using a pre-trained language model to achieve end-to-end event detection.  
Although the above methods have achieved significant improvements in event detection performance, most of them rely on contextual representations or structural information to enhance trigger word recognition. However, they lack sufficient semantic prior modeling of event types and do not incorporate effective semantic guidance mechanisms. As a result, the models often exhibit unstable recognition performance when dealing with events that involve complex semantics or blurred boundaries.