基于动态掩蔽注意力机制的事件抽取

事件抽取（event extraction）是自然语言处理（natural language processing，NLP）中的一个重要且有挑战性的任务，以完成从文本中识别出事件触发词（trigger）以及触发词对应的要素（argument）。对于一个句子中有多个事件的多事件抽取任务，提出了一种注意力机制的变种——动态掩蔽注意力机制（dynamic masked attention network，DyMAN），与常规注意力机制相比，动态掩蔽注意力机制能够捕捉更丰富的上下文表示并保留更有价值的信息。在ACE 2005数据集上进行的实验中，对于多事件抽取任务，与之前最好的模型JRNN相比，DyMAN模型在触发词分类任务上取得了9.8%的提升，在要素分类任务上取得了4.5%的提升，表明基于DyMAN的事件抽取模型在多事件抽取上能够实现领先的效果。

Event extraction based on dynamic masked attention

Event extraction is an important and challenging task in natural language processing(NLP), completing the identification of event triggers and their arguments from the text. For multiple-event extraction tasks with multiple events in a sentence, this paper proposed a model based on a variant of attention mechanism called dynamic masked attention network(DyMAN), which could capture richer context representation and keep more valuable information than the normal attention. The experiments demonstrate that the proposed model can achieve state-of-the-art performance on the ACE 2005 corpus. Compared with the previous best model named JRNN, the DyMAN model achieved a 9.8% improvement in trigger word classification tasks, and a 4.5% improvement in factor classification tasks.