# An Event Extraction Approach Based on a Multi-Round Q&A Framework
## 3 Models
### History Answer Embedding Strategies  
In this paper, we adopt a history answer embedding strategy, the basic idea of which is to use the answers of the previous round to construct the questions of the next round in a multi-round Q&A. The specific process is as follows: construct a multi-round question template, set the positions in the template as several blank slots for filling different contents in different rounds, obtain the historical answers of the previous round, input the marked historical answers and the corresponding marked positions in the sentences into the question template of the next round, and match the historical answers with the slots in the question template. The matching results are filled into the corresponding blank slots to generate the questions for the next round, which are fed into the answer extraction model to get the answers for that round, and the above process is repeated until the end of extraction.  
Specifically, the general steps to implement the history answer embedding strategy in the model are: first, the history answer is encoded to obtain the History Answer Embedding, which is embedded into the model as an additional input and spliced with the current question template to form a new input sequence to obtain the final input representation. By embedding the history answer into the model, it enables the model to make better use of the history answer information, thus improving the accuracy of event extraction.  
In the model, the answers extracted from the upstream rounds affect not only their own accuracy but also the construction of downstream questions, and the accuracy of each round of Q&A also has an impact on the performance of subsequent rounds. To address this issue, the multi-round Q&A framework needs to select an optimal text span to answer the questions. To select the text span, a strategy needs to be defined that determines the probability distribution of each span of text based on the current question and contextual information to improve the efficiency of the event extraction task. This algorithm is based on the output of the BMES tag, which assigns the probability of selecting spans to B (beginning), M (middle), and E (end) in the tag, respectively. Specifically, probability \(\{w_{1},w_{2},\ldots w_{n}\}\) of selecting a span is \(w_{1}\) assigned to the joint probability [19] of B, \(w_{2},\ldots w_{n-1}\) is assigned to the joint probability of all M tags including the selected span, and probability \(w_{n}\) is assigned to the joint probability of E, i.e., as shown in Equation (1):  
\[p(y(w_{1},\ldots w_{n})=answer|question,s)=p(w_{1}=B)\times p(w_{n}=E)\prod_{ i\in[2,n-1]}p(w_{i}=M) \tag{1}\]  
In addition, an empirical replay strategy [20] is used for each training batch. That means half of the samples are set to be simulated while the other half are randomly selected from the previously generated dataset. It helps to increase the diversity of the dataset and improve the generalization of the model and reduce the overfitting of the model, further improving the training efficiency and the performance of the model.