  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.
