Most approaches to extraction multiple relations from a paragraph require
multiple passes over the paragraph. In practice, multiple passes are
computationally expensive and this makes difficult to scale to longer
paragraphs and larger text corpora. In this work, we focus on the task of
multiple relation extraction by encoding the paragraph only once (one-pass). We
build our solution on the pre-trained self-attentive (Transformer) models,
where we first add a structured prediction layer to handle extraction between
multiple entity pairs, then enhance the paragraph embedding to capture multiple
relational information associated with each entity with an entity-aware
attention technique. We show that our approach is not only scalable but can
also perform state-of-the-art on the standard benchmark ACE 2005.

本研究针对多关系抽取任务，提出仅需一次编码的解决方案，结合预先训练的自我注意力（Transformer）模型、结构预测层和实体感知注意力技术，能够在处理更长段落和大型文本语料库上具有可扩展性，且在标准基准测试 ACE2005 上实现了最好的效果。