  Document-level Relation Extraction (DocRE) is the task of extracting all
semantic relationships from a document. While studies have been conducted on
English DocRE, limited attention has been given to DocRE in non-English
languages. This work delves into effectively utilizing existing English
resources to promote DocRE studies in non-English languages, with Japanese as
the representative case. As an initial attempt, we construct a dataset by
transferring an English dataset to Japanese. However, models trained on such a
dataset suffer from low recalls. We investigate the error cases and attribute
the failure to different surface structures and semantics of documents
translated from English and those written by native speakers. We thus switch to
explore if the transferred dataset can assist human annotation on Japanese
documents. In our proposal, annotators edit relation predictions from a model
trained on the transferred dataset. Quantitative analysis shows that relation
recommendations suggested by the model help reduce approximately 50% of the
human edit steps compared with the previous approach. Experiments quantify the
performance of existing DocRE models on our collected dataset, portraying the
challenges of Japanese and cross-lingual DocRE.
