MRC-FEE: Machine Reading Comprehension for Chinese Financial Event Extraction

The event extraction problem involves detecting event trigger words and extracting their corresponding event arguments. In contrast to general event extraction, financial event extraction focuses on financial texts, primarily at the document level. Existing methods mainly rely on a uniform sequence labeling model to identify event triggers and event arguments. However, this approach struggles with identifying nested and long entities and faces difficulties in handling complex event arguments. To address these challenges, we propose MRCFEE, a Chinese financial event extraction model based on machine reading comprehension (MRC). In the multi-turn MRC, the input to the model incorporates external knowledge and historical answers based on question templates and previous extraction results. Subsequently, we utilize a large-scale pre-trained language model in the financial field as the embedding layer and employ BiGRU to extract contextual information further. Then, we employ two binary classifiers to identify the probabilities of the starting and ending positions. Finally, we utilize the dynamic thresholding method to assess the rationality of the boundary of the obtained results. Experimental results on the DuEE-Fin dataset demonstrate that our model outperforms the previous methods, obtaining 84.1% and 75.6% F1 values for event detection and event argument extraction, respectively.