A Mixture of Experts with Adaptive Semantic Encoding for Event Detection
Zhongqiu Li, Yu Hong, Shiming He, Shuai Yang, Guodong Zhou · 2023
Event Detection (ED) is a challenging but valuable task. It aims to identify the words that trigger the events in text and classify them into pre-defined types. The previous works utilize entity information as supplementary clues for ED. However, word semantics in different contexts always have subtle variations. Capturing the connection between entity information and changing semantics is challenging. To tackle the issue, we propose the Mixture of Experts (MoE) technique with context clues to adaptively model semantics. We term the framework as CMoE. Concretely, the CMoE simultaneously performs entity and event detection with the MoE technique which are flexible components embedded transformer block. Furthermore, we apply multi-task learning to mine the shared knowledge between entity and event. We conduct experiments on the public ACE 2005 and KBP 2017 datasets. The results show that our model achieves competitive performance on ACE 2005 without using external knowledge, yielding an improvement of about 2.3% F1-score for ED. More importantly, our model outperforms all State-of-The-Art models on KBP 2017.