Understanding natural language involves recognizing how multiple event
mentions structurally and temporally interact with each other. In this process,
one can induce event complexes that organize multi-granular events with
temporal order and membership relations interweaving among them. Due to the
lack of jointly labeled data for these relational phenomena and the restriction
on the structures they articulate, we propose a joint constrained learning
framework for modeling event-event relations. Specifically, the framework
enforces logical constraints within and across multiple temporal and subevent
relations by converting these constraints into differentiable learning
objectives. We show that our joint constrained learning approach effectively
compensates for the lack of jointly labeled data, and outperforms SOTA methods
on benchmarks for both temporal relation extraction and event hierarchy
construction, replacing a commonly used but more expensive global inference
process. We also present a promising case study showing the effectiveness of
our approach in inducing event complexes on an external corpus.

提出了一种联合约束学习框架来对事件 - 事件关系进行建模，这种方法有效弥补了联合标记数据的缺失，并在时间关系提取和事件层级构建方面优于现有的方法，同时也可在外部语料库上诱导事件复合体的有效性。