  Relation Extraction (RE) has been extended to cross-document scenarios
because many relations are not simply described in a single document. This
inevitably brings the challenge of efficient open-space evidence retrieval to
support the inference of cross-document relations, along with the challenge of
multi-hop reasoning on top of entities and evidence scattered in an open set of
documents. To combat these challenges, we propose MR.COD (Multi-hop evidence
retrieval for Cross-document relation extraction), which is a multi-hop
evidence retrieval method based on evidence path mining and ranking. We explore
multiple variants of retrievers to show evidence retrieval is essential in
cross-document RE. We also propose a contextual dense retriever for this
setting. Experiments on CodRED show that evidence retrieval with MR.COD
effectively acquires crossdocument evidence and boosts end-to-end RE
performance in both closed and open settings.
