  Tagging based methods are one of the mainstream methods in relational triple
extraction. However, most of them suffer from the class imbalance issue
greatly. Here we propose a novel tagging based model that addresses this issue
from following two aspects. First, at the model level, we propose a three-step
extraction framework that can reduce the total number of samples greatly, which
implicitly decreases the severity of the mentioned issue. Second, at the
intra-model level, we propose a confidence threshold based cross entropy loss
that can directly neglect some samples in the major classes. We evaluate the
proposed model on NYT and WebNLG. Extensive experiments show that it can
address the mentioned issue effectively and achieves state-of-the-art results
on both datasets. The source code of our model is available at:
https://github.com/neukg/ConCasRTE.
