  In recent years, there is a surge of generation-based information extraction
work, which allows a more direct use of pre-trained language models and
efficiently captures output dependencies. However, previous generative methods
using lexical representation do not naturally fit document-level relation
extraction (DocRE) where there are multiple entities and relational facts. In
this paper, we investigate the root cause of the underwhelming performance of
the existing generative DocRE models and discover that the culprit is the
inadequacy of the training paradigm, instead of the capacities of the models.
We propose to generate a symbolic and ordered sequence from the relation matrix
which is deterministic and easier for model to learn. Moreover, we design a
parallel row generation method to process overlong target sequences. Besides,
we introduce several negative sampling strategies to improve the performance
with balanced signals. Experimental results on four datasets show that our
proposed method can improve the performance of the generative DocRE models. We
have released our code at https://github.com/ayyyq/DORE.
