  Distantly supervised models are very popular for relation extraction since we
can obtain a large amount of training data using the distant supervision method
without human annotation. In distant supervision, a sentence is considered as a
source of a tuple if the sentence contains both entities of the tuple. However,
this condition is too permissive and does not guarantee the presence of
relevant relation-specific information in the sentence. As such, distantly
supervised training data contains much noise which adversely affects the
performance of the models. In this paper, we propose a self-ensemble filtering
mechanism to filter out the noisy samples during the training process. We
evaluate our proposed framework on the New York Times dataset which is obtained
via distant supervision. Our experiments with multiple state-of-the-art neural
relation extraction models show that our proposed filtering mechanism improves
the robustness of the models and increases their F1 scores.
