  Determining coreference of concept mentions across multiple documents is a
fundamental task in natural language understanding. Previous work on
cross-document coreference resolution (CDCR) typically considers mentions of
events in the news, which seldom involve abstract technical concepts that are
prevalent in science and technology. These complex concepts take diverse or
ambiguous forms and have many hierarchical levels of granularity (e.g., tasks
and subtasks), posing challenges for CDCR. We present a new task of
Hierarchical CDCR (H-CDCR) with the goal of jointly inferring coreference
clusters and hierarchy between them. We create SciCo, an expert-annotated
dataset for H-CDCR in scientific papers, 3X larger than the prominent ECB+
resource. We study strong baseline models that we customize for H-CDCR, and
highlight challenges for future work.
