Event extraction has a trend in identifying event triggers and arguments in a unified framework, which has the advantage of avoiding the cascading failure in pipeline methods. The main problem is that joint models usually assume a one-to-one relationship between event triggers and arguments. It leads to the argument multiplexing problem, in which an argument mention can serve different roles in an event or shared by different events. To address this problem, we propose a multigraph-based event extraction framework. It allows parallel edges between any nodes, which is effective to represent semantic structures of an event. The framework enables the neural network to map a sentence(s) into a structurized semantic representation, which encodes multi-overlapped events. After evaluated on four public datasets, our method achieves the state-of-the-art performance, outperforming all compared models. Analytical experiments show that the multigraph representation is effective to address the argument multiplexing problem and helpful to advance the discriminability of the neural network for event extraction.

中文翻译：

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用于事件提取的多图表示


事件提取的趋势是在统一的框架中识别事件触发器和参数，其优点是避免管道方法中的级联失败。主要问题是联合模型通常假设事件触发器和参数之间存在一对一的关系。它导致了参数复用问题，其中参数提及可以在事件中充当不同的角色或由不同的事件共享。为了解决这个问题，我们提出了一种基于多图的事件提取框架。它允许任何节点之间的平行边，这可以有效地表示事件的语义结构。该框架使神经网络能够将句子映射为结构化语义表示，该表示对多重重叠事件进行编码。在对四个公共数据集进行评估后，我们的方法实现了最先进的性能，优于所有比较模型。分析实验表明，多图表示可以有效解决参数复用问题，有助于提高神经网络事件提取的辨别能力。