  Relating entities and events in text is a key component of natural language
understanding. Cross-document coreference resolution, in particular, is
important for the growing interest in multi-document analysis tasks. In this
work we propose a new model that extends the efficient sequential prediction
paradigm for coreference resolution to cross-document settings and achieves
competitive results for both entity and event coreference while provides strong
evidence of the efficacy of both sequential models and higher-order inference
in cross-document settings. Our model incrementally composes mentions into
cluster representations and predicts links between a mention and the already
constructed clusters, approximating a higher-order model. In addition, we
conduct extensive ablation studies that provide new insights into the
importance of various inputs and representation types in coreference.
