Canonical relation extraction aims to extract relational triples from
sentences, where the triple elements (entity pairs and their relationship) are
mapped to the knowledge base. Recently, methods based on the encoder-decoder
architecture are proposed and achieve promising results. However, these methods
cannot well utilize the entity information, which is merely used as augmented
training data. Moreover, they are incapable of representing novel entities,
since no embeddings have been learned for them. In this paper, we propose a
novel framework, Bi-Encoder-Decoder (BED), to solve the above issues.
Specifically, to fully utilize entity information, we employ an encoder to
encode semantics of this information, leading to high-quality entity
representations. For novel entities, given a trained entity encoder, their
representations can be easily generated. Experimental results on two datasets
show that, our method achieves a significant performance improvement over the
previous state-of-the-art and handle novel entities well without retraining.

本研究提出了一种新的框架，Bi-Encoder-Decoder (BED)，用于解决规范化关系提取中的实体信息利用、新实体表示、性能提升等问题。实验结果表明，该方法在两个数据集上性能显著优于之前的最优方法，并能够良好处理新实体而无需重新训练。