Semi-supervised Relation Extraction via Data Augmentation and
  Consistency-training
Komal K. Teru
Due to the semantic complexity of the Relation extraction (RE) task,
obtaining high-quality human labelled data is an expensive and noisy process.
To improve the sample efficiency of the models, semi-supervised learning (SSL)
methods aim to leverage unlabelled data in addition to learning from limited
labelled data points. Recently, strong data augmentation combined with
consistency-based semi-supervised learning methods have advanced the state of
the art in several SSL tasks. However, adapting these methods to the RE task
has been challenging due to the difficulty of data augmentation for RE. In this
work, we leverage the recent advances in controlled text generation to perform
high quality data augmentation for the RE task. We further introduce small but
significant changes to model architecture that allows for generation of more
training data by interpolating different data points in their latent space.
These data augmentations along with consistency training result in very
competitive results for semi-supervised relation extraction on four benchmark
datasets.