  Distantly supervised (DS) relation extraction (RE) has attracted much
attention in the past few years as it can utilize large-scale auto-labeled
data. However, its evaluation has long been a problem: previous works either
took costly and inconsistent methods to manually examine a small sample of
model predictions, or directly test models on auto-labeled data -- which, by
our check, produce as much as 53% wrong labels at the entity pair level in the
popular NYT10 dataset. This problem has not only led to inaccurate evaluation,
but also made it hard to understand where we are and what's left to improve in
the research of DS-RE. To evaluate DS-RE models in a more credible way, we
build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20,
and thoroughly evaluate several competitive models, especially the latest
pre-trained ones. The experimental results show that the manual evaluation can
indicate very different conclusions from automatic ones, especially some
unexpected observations, e.g., pre-trained models can achieve dominating
performance while being more susceptible to false-positives compared to
previous methods. We hope that both our manual test sets and novel observations
can help advance future DS-RE research.
