# Chinese Event Detection without Triggers Based on Dual Attention
## 3 Event Detection without Triggers Based on Dual Attention
### Trigger Simulation and Contextual Analysis  
Each type of event is triggered by a specific set of words called event trigger words. For example, seismic events are usually triggered by words like \"earthquake\", \"shaking\" and \"trembling\". Therefore, the event trigger words are important clues for this task. To avoid the trouble caused by event trigger words, we add local attention to simulate event trigger words so as to detect events without trigger words. BiLSTM captures the meaning of each word in the sentence. In addition, BiLSTM combines local attention to find the key information in the semantics and assigns each word weight according to the degree of importance. The word with the highest weight will be defaulted as the hidden event trigger word to prevent mismatch.  
BiLSTM is suitable for modeling sequence data. It can capture word context using forward and backward LSTM units, thus effectively capturing the semantics of each word in a sentence. We input the feature vector \(W\) into BiLSTM and output hidden states \(\overset{\rightarrow}{h}\) and \(\overset{\leftarrow}{h}\). To make full use of the semantics in hidden states, they are synthesized as \(h=\left[\overset{\rightarrow}{h},\overset{\leftarrow}{h}\right]\). The local attention vector \(\alpha_{s}\) is generated by the output vector \(h\) and the event type embedding vector \(t_{1}\), as shown in Equation (1).  
\[\alpha_{s}^{k}=\frac{\exp(h_{k}\cdot t_{1}^{T})}{\sum_{i=1}^{n}\exp(h_{i}\cdot t_{1 }^{T})} \tag{1}\]  
where \(h_{k}\) is the \(k\)th part of the output vector \(h\), \(\alpha_{s}^{k}\) is the \(k\)th part of the local attention vector \(\alpha_{s}\), and \(t_{1}^{T}\) is the transpose of the event type embedding vector. \(\alpha_{s}\) can mine the keywords in the sentence, so that the attention weight of the word that triggers the target event type is higher than other words, achieving the goal of simulating trigger words.  
Although local attention can solve the problem of trigger-word mismatch, it still cannot solve the polysemous problem. Global attention is introduced to solve the problem of ambiguous triggers. By learning the keywords in the sentence and the contextual information in the document, the unique meaning of the trigger word in the scene can be obtained to help judge the event type.  
The global attention embedding vector \(\alpha_{d}\) consists of three parts: the output vector \(h\), the event type embedding vector \(t_{2}\) and the document-level embedding vector \(d\) obtained by Doc2 Vec, which is shown in Equation (2).  
\[\alpha_{d}^{k}=\frac{\exp(h_{k}\cdot t_{2}^{T}\cdot d^{T})}{\sum_{i=1}^{n}\exp (h_{i}\cdot t_{2}^{T}\cdot d^{T})} \tag{2}\]  
where \(h_{k}\) is the \(k\)th part of the output vector \(h\), \(\alpha_{d}^{k}\) is the \(k\)th part of the global attention vector \(\alpha_{d}\), \(t_{2}^{T}\) is the event type embedding vector transpose, and \(d^{T}\) is the document-level embedding vector transpose.  
Although global attention can solve the polysemous problem, it dilutes the weight of keywords and impairs the model's judgment ability when there is too much noisy data in the document. Therefore, with their advantages and disadvantages, local and global attention cannot solve all problems alone. We weighted local and global attention to improve the event detection accuracy, calculated as shown in Equation (3).  
\[v_{s} =\alpha_{s}\cdot t_{1}\] \[v_{d} =\alpha_{d}\cdot t_{2} \tag{3}\] \[o =\sigma(\lambda\cdot v_{s}+(1-\lambda)\cdot v_{d})\]  
Among them, the final output value \(o\) consists of two components \(v_{s}\) and \(v_{d}\). \(v_{s}\) is generated by the dot product of \(\alpha_{s}\) and \(t_{1}\) for capturing local features and simulating hidden event trigger words; \(v_{d}\) is generated by the dot product of \(\alpha_{d}\) and \(t_{2}\) for capturing global features and context. \(\sigma\) is a sigmoid function, and \(\lambda\in[0,1]\) is a hyper-parameter that weighs between \(v_{s}\) and \(v_{d}\).