# Chinese Event Detection without Triggers Based on Dual Attention
###### Abstract  
In natural language processing, event detection is a critical step in event extraction, aiming to detect the occurrences of events and categorize them. Currently, the defects of Chinese event detection based on triggers include polysemous triggers and trigger-word mismatches, which reduce the accuracy of event detection models. Therefore, event detection without triggers based on dual attention (EDWTDA), a trigger-free model that can skip the trigger identification process and determine event types directly, is proposed to fix the problems mentioned above. EDWTDA adopts a dual attention mechanism, integrating local and global attention. Local attention captures key semantic information in sentences and simulates hidden event trigger words to solve the problem of trigger-word mismatch, while global attention digs for the context of documents, fixing the problem of polysemous triggers. Besides, event detection is transformed into a binary classification task to avoid problems caused by multiple tags. Meanwhile, the sample imbalance brought about by the transformation is settled with the application of the focal loss function. The experimental results on the ACE 2005 Chinese corpus show that, compared with the best baseline model, JMCEE, the accuracy rate, recall rate, and F1-score of the proposed model increased by 3.40%, 3.90%, and 3.67%, respectively.  
dual attention; Chinese event detection; binary classification +
Footnote †: journal:  
addition, event detection and extraction can be transformed into a question-and-answer task, extracting the event arguments directly in an end-to-end manner [12].  
In summary, in order to deal with the problems of polysemous triggers and trigger-word mismatch, this paper puts forward EDWTDA, a model that adopts local attention to capture keywords and sentence-level semantic information, simulating hidden event trigger words, as well as global attention to mine the context of documents, determining the true meaning of words. Eventually, it will be possible to skip the trigger identification and determine the event type directly. For example, S1 says, \"The car accident was so severe that he did not wake up despite the doctors' best efforts\", and S2 says, \"He left his family with remorse from then on\". Without considering the full context, S2 may express both a death event and a position change event. However, by considering the document information at the same time, the event type can be accurately classified as a death event. Therefore, the context of the full text can assist in determining the event type, which is of great significance for event detection and classification.  
The main contributions of this paper are as follows:  
1. We propose an event detection without triggers based on dual attention to address the problems of polysemous triggers and trigger-word mismatch in Chinese event detection methods based on trigger identification;
2. We transform the event detection task into a binary classification task. We use the focal loss function to solve the sample imbalance and gradient disappearance brought by the transformation;
3. We conducted extensive experiments on the ACE 2005 Chinese dataset and the dam safety operation log dataset. The experimental results show that our method outperforms existing classical event detection methods.  
The remainder of this paper is structured as follows: Section 2 investigates the literature review related to event detection. Section 3 then introduces the event detection method without triggers. Section 4 compares our model's results to those of other traditional methods. Section 5 concludes with some recommendations.