Event temporal relation extraction~(ETRE) is usually formulated as a
multi-label classification task, where each type of relation is simply treated
as a one-hot label. This formulation ignores the meaning of relations and wipes
out their intrinsic dependency. After examining the relation definitions in
various ETRE tasks, we observe that all relations can be interpreted using the
start and end time points of events. For example, relation \textit{Includes}
could be interpreted as event 1 starting no later than event 2 and ending no
earlier than event 2. In this paper, we propose a unified event temporal
relation extraction framework, which transforms temporal relations into logical
expressions of time points and completes the ETRE by predicting the relations
between certain time point pairs. Experiments on TB-Dense and MATRES show
significant improvements over a strong baseline and outperform the
state-of-the-art model by 0.3\% on both datasets. By representing all relations
in a unified framework, we can leverage the relations with sufficient data to
assist the learning of other relations, thus achieving stable improvement in
low-data scenarios. When the relation definitions are changed, our method can
quickly adapt to the new ones by simply modifying the logic expressions that
map time points to new event relations. The code is released at
https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE.

本文提出了一个统一的事件时间关系提取框架，并将时间关系转化成时间点的逻辑表达式，通过预测某些时间点的关系完成事件时间关系提取。该方法不仅在 TB-Dense 和 MATRES 上取得了显著提升，也可以在低数据情况下实现稳定的改进。