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.

翻译：序列生成通过在信息抽取中引入大规模预训练序列到序列（Seq2Seq）模型，在近期工作中展现出优异性能。本文探究了将序列生成应用于关系抽取的优势，发现以关系名称或其同义词作为生成目标时，其文本语义及词序模式间的相关性会影响模型性能。为此，我们提出标签增强关系抽取（RELA）方法——一种针对关系抽取任务具备自动标签增强功能的Seq2Seq模型。所谓标签增强，是指为每个关系名称生成语义相关的同义词作为生成目标。此外，我们深入分析了Seq2Seq模型处理关系抽取任务时的行为特性。实验结果表明，RELA在四个关系抽取数据集上均取得了与先前方法相当的竞争性结果。