  Joint entity and relation extraction is a process that identifies entity
pairs and their relations using a single model. We focus on the problem of
joint extraction in distantly-labeled data, whose labels are generated by
aligning entity mentions with the corresponding entity and relation tags using
a knowledge base (KB). One key challenge is the presence of noisy labels
arising from both incorrect entity and relation annotations, which
significantly impairs the quality of supervised learning. Existing approaches,
either considering only one source of noise or making decisions using external
knowledge, cannot well-utilize significant information in the training data. We
propose DENRL, a generalizable framework that 1) incorporates a lightweight
transformer backbone into a sequence labeling scheme for joint tagging, and 2)
employs a noise-robust framework that regularizes the tagging model with
significant relation patterns and entity-relation dependencies, then
iteratively self-adapts to instances with less noise from both sources.
Surprisingly, experiments on two benchmark datasets show that DENRL, using
merely its own parametric distribution and simple data-driven heuristics,
outperforms large language model-based baselines by a large margin with better
interpretability.
