  Relation extraction (RE) plays an important role in extracting knowledge from
unstructured text but requires a large amount of labeled corpus. To reduce the
expensive annotation efforts, semisupervised learning aims to leverage both
labeled and unlabeled data. In this paper, we review and compare three typical
methods in semi-supervised RE with deep learning or meta-learning:
self-ensembling, which forces consistent under perturbations but may confront
insufficient supervision; self-training, which iteratively generates pseudo
labels and retrain itself with the enlarged labeled set; dual learning, which
leverages a primal task and a dual task to give mutual feedback. Mean-teacher
(Tarvainen and Valpola, 2017), LST (Li et al., 2019), and DualRE (Lin et al.,
2019) are elaborated as the representatives to alleviate the weakness of these
three methods, respectively.
