A syntactic distance sensitive neural network for event argument extraction

Event argument extraction aims at identifying event arguments from texts as well as determining their respective roles in an event. Despite some neural networks applied for this task, their performance are still not satisfactory due to the following shortcomings. Syntactic information were not well explored; Event arguments were independently extracted; Pattern knowledge were not explicitly exploited. In this paper, we propose a Syntactic Distance Sensitive Neural Network model to tackle these problems. Our model first captures long-range dependencies in between event triggers and event arguments through performing graph convolution over syntactic trees, where we introduce syntactic distance to weight the importance of each word. Furthermore, we design an argument interaction module to mine argument-argument interactions according to the shortest dependency distances in between arguments. To enjoy pattern knowledge, we design a pattern-aware argument classification module to ensure the reasonability of extracted arguments. Extensive experiments have validated the superiority of the proposed model, which achieves the state-of-the-art results in terms of better F1-score on both argument identification and role classification.