  To alleviate human efforts from obtaining large-scale annotations,
Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in
addition to learning from limited samples. Existing self-training methods
suffer from the gradual drift problem, where noisy pseudo labels on unlabeled
data are incorporated during training. To alleviate the noise in pseudo labels,
we propose a method called MetaSRE, where a Relation Label Generation Network
generates quality assessment on pseudo labels by (meta) learning from the
successful and failed attempts on Relation Classification Network as an
additional meta-objective. To reduce the influence of noisy pseudo labels,
MetaSRE adopts a pseudo label selection and exploitation scheme which assesses
pseudo label quality on unlabeled samples and only exploits high-quality pseudo
labels in a self-training fashion to incrementally augment labeled samples for
both robustness and accuracy. Experimental results on two public datasets
demonstrate the effectiveness of the proposed approach.
