Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advancements in natural language processing, temporal relation classification remains a considerable challenge. Early attempts framed this task using a comprehensive set of temporal relations between events and temporal expressions. However, due to the task complexity, datasets have been progressively simplified, leading recent approaches to focus on the relations between event pairs and to use only a subset of relations. In this work, we revisit the broader goal of classifying interval relations between temporal entities by considering the full set of relations that can hold between two time intervals. The proposed approach, Interval from Point, involves first classifying the point relations between the endpoints of the temporal entities and then decoding these point relations into an interval relation. Evaluation on the TempEval-3 dataset shows that this approach can yield effective results, achieving a temporal awareness score of $70.1$ percent, a new state-of-the-art on this benchmark.

翻译：时序关系分类是一项确定文本中时间实体对之间时序关系的任务。尽管自然语言处理领域近期取得了进步，但时序关系分类仍是一个重大挑战。早期方法利用事件与时间表达式之间的全面时序关系集来处理该任务，然而受任务复杂性的影响，数据集不断简化，导致近期研究的重点转向事件对之间的关系，并且仅使用部分关系子集。本文重访更宏大的目标，即通过考虑两个时间区间之间可能存在的全部关系集，对时间实体间的区间关系进行分类。所提出的“从点映射区间”(Interval from Point)方法，首先对时间实体端点之间的点关系进行分类，随后将这些点关系解码为区间关系。在TempEval-3数据集上的评估表明，该方法能产生有效结果，时序感知得分达70.1%，在该基准测试上创下新的最佳性能。