# Chinese Event Detection without Triggers Based on Dual Attention
## 2 Related Work  
### Event Detection Based on Trigger Identification  
Deep learning has recently become popular for automatically mining text features. [PERSON] and [PERSON] used a convolutional neural network (CNN) to represent the window around candidate trigger words [13] and used the global maximum pool to summarize the extracted features before passing them to a linear classifier for event classification. Subsequently, discontinuous convolution was introduced to skip insignificant words in word sequences to improve the accuracy and recall of event detection [14]. Although CNN could effectively capture syntactic and semantic information between words in a sentence, it could not encode the meaning of words in different contexts. Hybrid neural networks combined CNN and BiLSTM to capture sequence and block information in a specific context for prediction [15], but they were still incapable of disambiguating certain events [16]. The gated multi-lingual attention (GMLATT) system introduced attention mechanisms to mine coherent information in multilingual data [4], alleviating data scarcity and monolingual ambiguity. On the basis of deep learning, adding features related to event trigger words can improve the accuracy rate and recall rate of recognition. For example, combining the sequential features of entity types and word sequence features can help the model find the in-sentence triggers [17]. For event detection, the syntactic relationship representation based on dependency trees reflects the interrelationship between candidate trigger words and related entities better than the sentence representation. Dependency tree-based graph convolutional networks and attention mechanisms allowed explicit modeling and aggregation of multi-order syntactic representations in sentences [5]. However, this ignored the dependency label information. [PERSON] used gate control to make up for the insufficient consideration of candidate keywords in GCN, and he calculated the correlation with trigger words to indicate the importance of words [18]. [PERSON] integrated dependency relationships between words in syntactic parsing trees and dependency labels into graph transformer networks (GTN) to improve the precision and complexity of the model [19]. The scarcity of annotated data is a great challenge for event detection [20]. By averaging the performance of computing event prototypes and consuming only one of the events mentioned, the dynamic-memory-based prototyping network (DMB-PN) can produce more robust sentence encoding and solve the few-shot problem [21]. [PERSON] et al. have made full use of the matching information between supporting samples in few-shot learning by calculating the intra-class and inter-class distance based on metric learning [22]. Known types of annotations can be used for semi-supervised learning to reduce manual labeling costs [23]. Likewise, external open-domain trigger knowledge can alleviate data sparsity and reduce the built-in bias of high-frequency trigger words in annotations [24], but it relies on complex, predefined rules and existing instances in the knowledge base and has problems such as a low coverage rate, subject bias, and data noise. Targeting the above problems, [PERSON] et al. constructed a large event-related candidate set with high coverage and then applied an adversarial training mechanism to iteratively identify informative events from the candidate set while filtering out noisy events [25]. The traditional ACE event detection methods treat multiple events in a sentence as independent events and identify them separately using sentence-level information. However, events in a sentence are typically interdependent, and sentence-level information is frequently insufficient to reduce the ambiguity of certain events. Hierarchical and bias tagging networks with gated multi-level attention mechanisms (HBTNGMA) can simultaneously solve these two problems, realize automatic extraction and dynamic fusion of sentence-level and document-level information, and detect multiple events in a sentence [26].