论文标题
端到端神经事件核心分辨率
End-to-End Neural Event Coreference Resolution
论文作者
论文摘要
传统的事件核心系统通常依赖于管道框架和手工制作的功能，这些功能通常会面临错误传播问题，并且具有较差的概括能力。在本文中，我们提出了一种端到端事件核心方法-E3C神经网络，该方法可以共同建模事件检测和事件核心分辨率分辨率任务，并学会自动从原始文本中提取功能。此外，由于事件提到的是高度多样化的，并且事件核心由长距离，语义依赖性的决策精致控制，因此在我们的E3C神经网络中进一步提出了类型引导的事件核心机制。实验表明，我们的方法在两个标准数据集上实现了新的最新性能。
Traditional event coreference systems usually rely on pipeline framework and hand-crafted features, which often face error propagation problem and have poor generalization ability. In this paper, we propose an End-to-End Event Coreference approach -- E3C neural network, which can jointly model event detection and event coreference resolution tasks, and learn to extract features from raw text automatically. Furthermore, because event mentions are highly diversified and event coreference is intricately governed by long-distance, semantic-dependent decisions, a type-guided event coreference mechanism is further proposed in our E3C neural network. Experiments show that our method achieves new state-of-the-art performance on two standard datasets.
