Abstract
Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.
Abstract (translated)
识别事件之间的时间关系是自然语言理解的重要一步。然而，一个故事中两个事件之间的时间关系取决于其他事件之间的关系，并且通常由其他事件之间的关系决定。因此，即使对人类注释学家来说，有效地识别事件之间的时间关系也是一个具有挑战性的问题。本文认为，在学习识别这些关系时，必须考虑这些依赖关系，并提出一种结构化的学习方法来解决这一挑战。作为副产品，这为处理缺失关系提供了新的视角，这是一个损害现有方法的已知问题。正如我们所展示的，所提出的方法对这个问题的两个常用数据集进行了显著的改进。
URL
https://arxiv.org/abs/1906.04943
