  The overarching objective of this paper is two-fold. First, to explore
model-based approaches to characterize the primary cause of the noise. in the
RE dataset TACRED Second, to identify the potentially noisy instances. Towards
the first objective, we analyze predictions and performance of state-of-the-art
(SOTA) models to identify the root cause of noise in the dataset. Our analysis
of TACRED shows that the majority of the noise in the dataset originates from
the instances labeled as no-relation which are negative examples. For the
second objective, we explore two nearest-neighbor-based strategies to
automatically identify potentially noisy examples for elimination and
reannotation. Our first strategy, referred to as Intrinsic Strategy (IS), is
based on the assumption that positive examples are clean. Thus, we have used
false-negative predictions to identify noisy negative examples. Whereas, our
second approach, referred to as Extrinsic Strategy, is based on using a clean
subset of the dataset to identify potentially noisy negative examples. Finally,
we retrained the SOTA models on the eliminated and reannotated dataset. Our
empirical results based on two SOTA models trained on TACRED-E following the IS
show an average 4% F1-score improvement, whereas reannotation (TACRED-R) does
not improve the original results. However, following ES, SOTA models show the
average F1-score improvement of 3.8% and 4.4% when trained on respective
eliminated (TACRED-EN) and reannotated (TACRED-RN) datasets respectively. We
further extended the ES for cleaning positive examples as well, which resulted
in an average performance improvement of 5.8% and 5.6% for the eliminated
(TACRED-ENP) and reannotated (TACRED-RNP) datasets respectively.

Computation and Language;Artificial Intelligence