  General purpose relation extraction has recently seen considerable gains in
part due to a massively data-intensive distant supervision technique from
Soares et al. (2019) that produces state-of-the-art results across many
benchmarks. In this work, we present a methodology for collecting high quality
training data for relation extraction from unlabeled text that achieves a
near-recreation of their zero-shot and few-shot results at a fraction of the
training cost. Our approach exploits the predictable distributional structure
of date-marked news articles to build a denoised corpus -- the extraction
process filters out low quality examples. We show that a smaller multilingual
encoder trained on this corpus performs comparably to the current
state-of-the-art (when both receive little to no fine-tuning) on few-shot and
standard relation benchmarks in English and Spanish despite using many fewer
examples (50k vs. 300mil+).
