# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 4 Proposed Method
### Argument Identification  
In this stage, we need to match and fill in the arguments and event roles for the existing events. Following [PERSON] et al. [10], we use a multi-granularity decoder to extract events in a parallel manner. This method consists of three parts: an event decoder, a role decoder, and an event-to-role decoder. The event decoder is designed to support the parallel extraction of all events and is used to model interactions between events. A learnable query matrix \(Q^{event}\in R^{k\times d}\) is generated for event extraction, where \(k\) represents the number of events contained in the document and is a hyperparameter. The event query matrix \(Q^{event}\) is then passed through a non-autoregressive decoder, which consists of multiple identical stacks of Transformer layers. In each layer, there is a multi-head self-attention mechanism that simulates interactions between events. Additionally, there is a multi-head cross-attention mechanism that integrates the document-aware representation \(H^{S}\) into the event query \(Q^{event}\):  
\[H^{event}=Event-Decoder\left(Q^{event};H^{S}\right) \tag{13}\]  
where \(H^{event}\in R^{k\times d}\). The role decoder is designed in a similar way to support parallel filling of all roles in the event and modeling interactions between roles. A learnable query matrix \(Q^{Role}\in R^{n\times d}\) is generated for event extraction, where \(n\) represents the number of roles associated with the corresponding event. Then, the role query matrix \(Q^{Role}\) is fed into a decoder with the same architecture as the event decoder. Specifically, the self-attention mechanism can model relationships between roles, and the cross-attention mechanism can integrate candidate entity representations \(H^{E}\) with the document representation. \[H^{Role}=Role-Decoder\Big{(}Q^{Role};H^{E}\Big{)} \tag{14}\]  
where \(H^{Role}\in R^{n\times d}\). In order to generate different events and their corresponding roles, we designed an event-to-role decoder to simulate the interaction between event information and role information. \[H^{event2 Role}=Event2 Role-Decoder\Big{(}H^{event};H^{Role}\Big{)} \tag{15}\]  
where \(H^{event2 Role}\in R^{k\times n\times d}\). Finally, after decoding with a multi-granularity decoder, we transform \(k\) event queries and \(n\) role queries into \(k\) predicted events and their corresponding \(n\) predicted roles. To filter out false events, we assess whether each predicted event is non-empty. Specifically, predicted events can be obtained through the following approach:  
\[p^{event}=softmax\big{(}H^{event}W_{e}\big{)} \tag{16}\]  
where \(W_{e}\in R^{d\times 2}\) is a learnable matrix. Afterwards, for each predicted event with predefined roles, we decode the predicted arguments by filling the candidate indices or null values with an \(N_{E}+1\) class classifier. \[p^{role}=softmax\Big{(}tanh(H^{event2 Role}W_{1}+H^{E}W_{2})v\Big{)} \tag{17}\]  
where \(W_{1}\in R^{d\times d}\), \(W_{2}\in R^{d\times d}\), and \(v\in R^{d}\) are learnable matrices, and \(p^{role}\in R^{k\times n\times(N_{E}+1)}\). It is worth noting that we carried out some dimensional transformations during the actual operation process. So far, we have obtained \(k\) predicted events, \(\hat{Y}=\hat{y}_{i=1}^{k}\) and the candidate entities for each role corresponding to each event, \(\hat{y}_{i}^{t}=\big{(}P_{i}^{1},P_{i}^{2},\dots,P_{i}^{n}\big{)}\). This completes the event extraction, as well as the identification, matching, and filling of the corresponding arguments.