One of the major challenges in coreference resolution is how to make use of entity-level features defined over clusters of mentions rather than mention pairs. However, coreferent mentions usually spread far apart in an entire text, which makes it extremely difficult to incorporate entity-level features. We propose a graph neural network-based coreference resolution method that can capture the entity-centric information by encouraging the sharing of features across all mentions that probably refer to the same real-world entity. Mentions are linked to each other via the edges modeling how likely two linked mentions point to the same entity. Modeling by such graphs, the features between mentions can be shared by message passing operations in an entity-centric manner. A global inference algorithm up to second-order features is also presented to optimally cluster mentions into consistent groups. Experimental results show our graph neural network-based method combing with the second-order decoding algorithm (named GNNCR) achieved close to state-of-the-art performance on the English CoNLL-2012 Shared Task dataset.

翻译：共指消解的主要挑战之一在于如何利用基于提及簇而非提及对的实体级特征。然而，共指提及通常在整篇文本中分布较远，这使得融合实体级特征极为困难。我们提出一种基于图神经网络的共指消解方法，通过促进所有可能指向同一真实世界实体的提及之间进行特征共享，从而捕捉实体中心信息。提及之间通过边缘相互连接，这些边缘建模了相关联提及指向同一实体的可能性。通过此类图的建模，提及之间的特征可以通过消息传递操作以实体中心的方式进行共享。此外，还提出了一种包含二阶特征的全局推理算法，以最优方式将提及聚类为一致的簇。实验结果表明，我们基于图神经网络的结合二阶解码算法（命名为GNNCR）的方法在英文CoNLL-2012共享任务数据集上取得了接近当前最优的性能。