Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering

Abstract

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 selfensemble 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.

Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction

“…Hao et al (2021) adopts adversarial training to filter noisy instances in the dataset. Nayak et al (2021) designs a self-ensemble framework to filter noisy instances despite information loss. proposes a hierarchical contrastive learning framework to reduce the effect of noise.…”