Event Detection (ED) aims to recognize instances of specified types of event
triggers in text. Different from English ED, Chinese ED suffers from the
problem of word-trigger mismatch due to the uncertain word boundaries. Existing
approaches injecting word information into character-level models have achieved
promising progress to alleviate this problem, but they are limited by two
issues. First, the interaction between characters and lexicon words is not
fully exploited. Second, they ignore the semantic information provided by event
labels. We thus propose a novel architecture named Label enhanced Heterogeneous
Graph Attention Networks (L-HGAT). Specifically, we transform each sentence
into a graph, where character nodes and word nodes are connected with different
types of edges, so that the interaction between words and characters is fully
reserved. A heterogeneous graph attention networks is then introduced to
propagate relational message and enrich information interaction. Furthermore,
we convert each label into a trigger-prototype-based embedding, and design a
margin loss to guide the model distinguish confusing event labels. Experiments
on two benchmark datasets show that our model achieves significant improvement
over a range of competitive baseline methods.

本文提出了一种使用标签增强的异构图注意力网络 (L-HGAT) 来解决中文事件检测中的单词触发器不匹配问题的方法。在该方法中，每个句子被转换成一个图，其中包含了单词和字符之间的交互作用，并使用异构图注意力网络来传播关系信息和丰富信息交互。此外，每个标签都被转换为基于触发器原型的嵌入。在两个基准数据集上进行的实验表明，该模型比其他基线方法取得了显着的改进。