Class-Adaptive Self-Training for Relation Extraction with Incompletely
  Annotated Training Data
Qingyu Tan Lu Xu Lidong Bing Hwee Tou Ng
Relation extraction (RE) aims to extract relations from sentences and
documents. Existing relation extraction models typically rely on supervised
machine learning. However, recent studies showed that many RE datasets are
incompletely annotated. This is known as the false negative problem in which
valid relations are falsely annotated as 'no_relation'. Models trained with
such data inevitably make similar mistakes during the inference stage.
Self-training has been proven effective in alleviating the false negative
problem. However, traditional self-training is vulnerable to confirmation bias
and exhibits poor performance in minority classes. To overcome this limitation,
we proposed a novel class-adaptive re-sampling self-training framework.
Specifically, we re-sampled the pseudo-labels for each class by precision and
recall scores. Our re-sampling strategy favored the pseudo-labels of classes
with high precision and low recall, which improved the overall recall without
significantly compromising precision. We conducted experiments on
document-level and biomedical relation extraction datasets, and the results
showed that our proposed self-training framework consistently outperforms
existing competitive methods on the Re-DocRED and ChemDisgene datasets when the
training data are incompletely annotated. Our code is released at
https://github.com/DAMO-NLP-SG/CAST.