Dependency trees help relation extraction models capture long-range relations between
words. However, existing dependency-based models either neglect crucial information (eg …

Organized relational knowledge in the form of “knowledge graphs” is important for many
applications. However, the ability to populate knowledge bases with facts automatically …

Adversarial training is a mean of regularizing classification algorithms by generating
adversarial noise to the training data. We apply adversarial training in relation extraction …

We present the first human-annotated dialogue-based relation extraction (RE) dataset
DialogRE, aiming to support the prediction of relation (s) between two arguments that …

We consider the task of relation classification, and pose this task as one of textual
entailment. We show that this formulation leads to several advantages, including the ability …

In this paper, we address two different types of noise in information extraction models: noise
from distant supervision and noise from pipeline input features. Our target tasks are entity …

Distant supervised relation extraction has been successfully applied to large corpus with
thousands of relations. However, the inevitable wrong labeling problem by distant …

CRISPR/Cas9 is a widely used genome editing tool for site-directed modification of
deoxyribonucleic acid (DNA) nucleotide sequences. However, how to accurately predict and …

Extracting information about entities remains an important research area. This paper
addresses the problem of corpus-level entity typing, ie, inferring from a large corpus that an …

This paper describes how to apply self-attention with relative positional encodings to the
task of relation extraction. We propose to use the self-attention encoder layer together with …