Empower Chinese event detection with improved atrous convolution neural networks.

Event Detection (ED) is an essential and challenging task in Information Extraction (IE). Recent advances in neural networks make it possible to build reliable models without complicated feature engineering. Neural network-based models commonly regard event detection as a char-wise or word-wise labeling task, which suffers from the problems of long-distance information miss-capturing, discontinuous labeling errors, etc., between characters/words and event triggers, especially in languages without natural word delimiters such as Chinese. In this paper, we propose a novel multi-layer Residual and Gated-Based Atrous Convolution Neural Network (RG-ACNN) framework, which attempts to alleviate above-mentioned problems. Specifically, the ACNN is introduced in our model to expand the receptive field to obtain multi-scale context information to capture dependencies between long-distance information. While gated and residual mechanisms are both imported to ACNN to improve our networks' capability of the information filtering and aggregation. Besides, RG-ACNN performs event detection in a char-wise paradigm, where a novel "head-tail dual-pointer" labeled strategy is used to overcome the incomplete continuous labeling problem. Experiments on the ACE2005-CN and several standard benchmark datasets show that RG-ACNN significantly outperforms state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]