Distantly-Supervised Joint Entity and Relation Extraction with
  Noise-Robust Learning
Yufei Li Xiao Yu Yanghong Guo Yanchi Liu Haifeng Chen Cong Liu
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
training these models on distantly-labeled data, which is generated by aligning
entity mentions in a text corpus with their corresponding entity and relation
types in a knowledge base. One key challenge here is the presence of noisy
labels, which arises from both entity and relation annotations, and
significantly impair the effectiveness of supervised learning applications.
However, existing research primarily addresses only one type of noise, thereby
limiting the effectiveness of noise reduction. To fill this gap, we introduce a
new noise-robust approach, that 1)~incorporates a pre-trained GPT-2 into a
sequence tagging scheme for simultaneous entity and relation detection, and
2)~employs a noise-robust learning framework which includes a new loss function
that penalizes inconsistency with both significant relation patterns and
entity-relation dependencies, as well as a self-adaptive learning step that
iteratively selects and trains on high-quality instances. Experiments on two
datasets show that our method outperforms the existing state-of-the-art methods
in both joint extraction performance and noise reduction effect.