  Relation extraction is a type of information extraction task that recognizes
semantic relationships between entities in a sentence. Many previous studies
have focused on extracting only one semantic relation between two entities in a
single sentence. However, multiple entities in a sentence are associated
through various relations. To address this issue, we propose a relation
extraction model based on a dual pointer network with a multi-head attention
mechanism. The proposed model finds n-to-1 subject-object relations using a
forward object decoder. Then, it finds 1-to-n subject-object relations using a
backward subject decoder. Our experiments confirmed that the proposed model
outperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus
and an F1-score of 78.3% for the NYT corpus.
