Event Temporal Relation Extraction (ETRE) aims to identify the temporal relationship between two events, which plays an important role in natural language understanding. Most previous works follow a single-label classification style, classifying an event pair into either a specific temporal relation (e.g., \textit{Before}, \textit{After}), or a special label \textit{Vague} when there may be multiple possible temporal relations between the pair. In our work, instead of directly making predictions on \textit{Vague}, we propose a multi-label classification solution for ETRE (METRE) to infer the possibility of each temporal relation independently, where we treat \textit{Vague} as the cases when there is more than one possible relation between two events. We design a speculation mechanism to explore the possible relations hidden behind \textit{Vague}, which enables the latent information to be used efficiently. Experiments on TB-Dense, MATRES and UDS-T show that our method can effectively utilize the \textit{Vague} instances to improve the recognition for specific temporal relations and outperforms most state-of-the-art methods.

翻译：事件时序关系抽取（ETRE）旨在识别两个事件之间的时序关系，在自然语言理解中具有重要作用。先前研究大多遵循单标签分类范式，将事件对分类为特定时序关系（如\textit{Before}、\textit{After}），或在可能存在多种时序关系时使用特殊标签\textit{Vague}。本文提出一种多标签分类解决方案（METRE），通过独立推断每种时序关系的可能性来处理ETRE任务，其中将\textit{Vague}定义为两个事件间存在多种可能关系的情形。我们设计了一种推测机制来探索\textit{Vague}背后隐藏的可能关系，从而有效利用潜在信息。在TB-Dense、MATRES和UDS-T数据集上的实验表明，本方法能有效利用\textit{Vague}实例提升特定时序关系的识别能力，其性能优于多数现有先进方法。