  Determining coreference of concept mentions across multiple documents is
fundamental for natural language understanding. Work on cross-document
coreference resolution (CDCR) typically considers mentions of events in the
news, which do not often 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 for concepts in scientific papers, with the goal of jointly inferring
coreference clusters and hierarchy between them. We create SciCo, an
expert-annotated dataset for this task, which is 3X larger than the prominent
ECB+ resource. We find that tackling both coreference and hierarchy at once
outperforms disjoint models, which we hope will spur development of joint
models for SciCo.
