论文标题
砂粒：文档级事件实体提取的生成角色填充变压器
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction
论文作者
论文摘要
我们重新审视文档级角色填充实体提取（REE）的经典问题，以进行模板填充。我们认为，句子级别的方法不适合任务，并引入了基于生成变压器的编码器框架（grit），该框架旨在在文档级别建模上下文：它可以在句子范围内做出提取决策；隐含地意识到名词短语核心结构，并且具有尊重模板结构中的交叉依赖性的能力。我们在MUC-4数据集上评估了我们的方法，并表明我们的模型的性能比先前的工作要好得多。我们还表明，我们的建模选择有助于模型性能，例如，通过隐式捕获语言知识，例如识别核心实体提及。
We revisit the classic problem of document-level role-filler entity extraction (REE) for template filling. We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoder-decoder framework (GRIT) that is designed to model context at the document level: it can make extraction decisions across sentence boundaries; is implicitly aware of noun phrase coreference structure, and has the capacity to respect cross-role dependencies in the template structure. We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work. We also show that our modeling choices contribute to model performance, e.g., by implicitly capturing linguistic knowledge such as recognizing coreferent entity mentions.
