# An Event Extraction Approach Based on a Multi-Round Q&A Framework
## 4 Experiment
### Comparison Experiments  
In order to verify the overall performance of the model, several models that have achieved better experimental results in recent years are used in this section as a benchmark for comparison with the model in this paper. The specific model is described as follows:  
* JointBeam: A classical event extraction algorithm based on traditional machine learning using manually designed features based on structured predictions to extract events. In this paper, we select the model as a control group for feature-engineering-based event extraction methods;
* dbRNN: A bi-directional long- and short-term memory network based event extraction method. utilizing the method of adding relational dependency bridges to the BiLSTM for event extraction. This method is more representative before the advent of pre-trained models and is often used as a baseline in the same type of studies. Therefore, this model is selected as a control group for non-machine reading-comprehension-based event extraction methods in this paper;
* BERT_QA: An event extraction based on question and answer model, which is an earlier proposal to construct the event extraction task as a question and answer (QA)/machine reading comprehension (MRC) task. In this paper, the model is extended on the basis of this model, so it is selected as a control group;
* MQAEE: A bert-based multi-round Q&A type event extraction. The idea of multi-round Q&A in this method has similarity with this paper, but its multi-round Q&A is framed as independent rounds of Q&A, and the dependency between argument elements is not fully considered, so the model has been selected as a control group against the effectiveness of the historical answer information embedding strategy in this paper. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Trigger Identification (\%)**} & \multicolumn{3}{c}{**Trigger Identification**} \\  & & & & \multicolumn{3}{c}{**+ Classification (\%)**} \\ \hline Methods & P & R & F1 & P & R & F1 \\ \hline Ours\_NoPre & 72.10 & 75.02 & 73.53 & 70.43 & 71.38 & 70.90 \\ Ours & **75.32** & **78.26** & **76.76** & **74.19** & **74.22** & **74.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Trigger word recognition and classification results based on cross-domain knowledge pre-training. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Argument Identification (\%)**} & \multicolumn{3}{c}{**Argument Identification**} \\  & & & & **+ Classification (\%)** \\ \hline Methods & P & R & F1 & P & R & F1 \\ \hline Ours\_NoPre & 65.68 & 60.17 & 62.80 & 64.42 & 54.27 & 58.91 \\ Ours & **69.60** & **61.52** & **65.31** & **67.67** & **58.43** & **62.71** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recognition and classification results of event elements based on cross-domain knowledge pre-training. The final experimental results obtained are shown in Tables 6 and 7, which list the experimental results of comparing the algorithm in this paper with other benchmark methods in the trigger word recognition extraction task and the event element recognition extraction task. From the above experimental results, the following three conclusions can be drawn:  
(1) Comparing the neural-network-based event extraction approach (dbRNN) and the feature-engineering-based approach (JointBeam), it can be found that the neural-network-based model has a clear advantage because the neural network can better learn the semantic feature representation of the sentence, so it can achieve better performance. (2) Comparing non-machine reading-comprehension-based event extraction methods (dbRNN) and machine reading-comprehension-based methods (BERT_QA, MQAEE), it can be found that models based on machine reading comprehension methods outperform those based on classification or sequence annotation because the former can encode a priori information in the problem, allowing the model to focus more on the extraction of specific information in the passage, thus effectively improving the performance of event extraction.