Causal Event Extraction using Iterated Dilated Convolutions with Semantic Convolutional Filters

Jianqi Gao

Abstract: Abstract—Causal Event Extraction (CEE) is a joint extraction task of events and causality, which can help text understanding, event prediction and so on. Recent research has achieved stateof-the-art performance in various Natural Language Processing (NLP) tasks by combining pre-trained models with neural networks. However, ambiguity of event description and long-distance dependence of event causality result in the low accuracy of extractors. In this paper, we propose a model to incorporate in-domain knowledge by taking frequent expression of event causality into account, and use iterated dilated convolutions to expand the perception field of event causality. External causal knowledge is modeled as frequent n-grams with different length, which is used as convolution filters during kernel initialization, enhancing the ability of model to capture the boundary of event description. To obtain long-distance dependence of event causality, we use iterated dilated convolutions to aggregate context from the entire sentence. Experimental results show that our method significantly outperform the baselines with faster convergence speed.