  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.
