Document-level event causality identification (DECI) requires identifying causal relations across a document, where inter-sentence causality is often implicit and supported by weak evidence. Existing methods typically construct event graphs from pairwise predictions, which may overlook weak but valid causal edges, and thus hinder causal message passing. To address these issues, we propose ICGR, an Iterative Causal Graph Refinement framework that iteratively refines event graphs by preserving low-confidence edges and performing message passing on the refined graph to produce a more complete and reliable structure. In each refinement round, the Constrained Edge Completion module identifies missing causal edges, and the Conditional Diffusion module refines low-confidence edges through denoising. Experiments on EventStoryLine and MAVEN-ERE show that ICGR achieves state-of-the-art performance, with notable improvements on inter-sentence causal relations.

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.