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.
翻译:事件检测( ED) 旨在识别文本中特定类型的事件触发器。 不同于英语 ED, 中国 ED 存在字触发错配问题, 原因是字边界不确定。 将字信息注入字符级模型的做法已经取得了大有希望的进展, 缓解了这一问题, 但受到两个问题的限制 。 首先, 字符和词汇词的互动没有得到充分利用 。 其次, 它们忽略了事件标签提供的语义信息 。 因此, 我们提议了一个名为 Label 增强异质图形注意网络( L- HGAT) 的新结构 。 具体地说, 我们将每个句子转换成图表, 字符节点和字节点与不同类型的边缘连接, 从而完全保留了字词和字符之间的互动。 然后引入一个多式图形关注网络来传播关联信息信息互动。 此外, 我们将每个标签转换成一个基于触发- 方案型的嵌嵌入, 并设计一个差值损失值以指导模型区分混淆事件标签 。 在两个基准数据集上进行的实验显示, 我们的模型在一系列竞争性基线方法上取得了显著的改进 。