论文标题
核心分辨率的图表完善
Graph Refinement for Coreference Resolution
论文作者
论文摘要
核心分辨率的最新模型基于独立提及的成对决策。我们提出了一种建模方法，该方法了解文档级别的重点并做出全球决策。为此，我们在图形结构中建模核心链接，其中节点是文本中的令牌，而边缘表示它们之间的关系。我们的模型以非自动回归方式预测图形，然后根据先前的预测进行迭代完善，从而允许决策之间的全局依赖关系。实验结果表明，对各种基线的改进，增强了文档级信息改善会议解决方案的假设。
The state-of-the-art models for coreference resolution are based on independent mention pair-wise decisions. We propose a modelling approach that learns coreference at the document-level and takes global decisions. For this purpose, we model coreference links in a graph structure where the nodes are tokens in the text, and the edges represent the relationship between them. Our model predicts the graph in a non-autoregressive manner, then iteratively refines it based on previous predictions, allowing global dependencies between decisions. The experimental results show improvements over various baselines, reinforcing the hypothesis that document-level information improves conference resolution.
