  Multiple instance learning (MIL) has become the standard learning paradigm
for distantly supervised relation extraction (DSRE). However, due to relation
extraction being performed at bag level, MIL has significant hardware
requirements for training when coupled with large sentence encoders such as
deep transformer neural networks. In this paper, we propose a novel sampling
method for DSRE that relaxes these hardware requirements. In the proposed
method, we limit the number of sentences in a batch by randomly sampling
sentences from the bags in the batch. However, this comes at the cost of losing
valid sentences from bags. To alleviate the issues caused by random sampling,
we use an ensemble of trained models for prediction. We demonstrate the
effectiveness of our approach by using our proposed learning setting to
fine-tuning BERT on the widely NYT dataset. Our approach significantly
outperforms previous state-of-the-art methods in terms of AUC and P@N metrics.
