  Sequence generation demonstrates promising performance in recent information
extraction efforts, by incorporating large-scale pre-trained Seq2Seq models.
This paper investigates the merits of employing sequence generation in relation
extraction, finding that with relation names or synonyms as generation targets,
their textual semantics and the correlation (in terms of word sequence pattern)
among them affect model performance. We then propose Relation Extraction with
Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation
for RE. By saying label augmentation, we mean prod semantically synonyms for
each relation name as the generation target. Besides, we present an in-depth
analysis of the Seq2Seq model's behavior when dealing with RE. Experimental
results show that RELA achieves competitive results compared with previous
methods on four RE datasets.
