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Determining coreference of concept mentions across multiple documents is afundamental task in natural language understanding. Previous work oncross-document coreference resolution (CDCR) typically considers mentions ofevents in the news, which seldom involve abstract technical concepts that areprevalent in science and technology. These complex concepts take diverse orambiguous forms and have many hierarchical levels of granularity (e.g., tasksand subtasks), posing challenges for CDCR. We present a new task ofHierarchical CDCR (H-CDCR) with the goal of jointly inferring coreferenceclusters and hierarchy between them. We create SciCo, an expert-annotateddataset for H-CDCR in scientific papers, 3X larger than the prominent ECB+resource. We study strong baseline models that we customize for H-CDCR, andhighlight challenges for future work.

SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts