  The extraction of interactions between chemicals and proteins from several
biomedical articles is important in many fields of biomedical research such as
drug development and prediction of drug side effects. Several natural language
processing methods, including deep neural network (DNN) models, have been
applied to address this problem. However, these methods were trained with
hard-labeled data, which tend to become over-confident, leading to degradation
of the model reliability. To estimate the data uncertainty and improve the
reliability, "calibration" techniques have been applied to deep learning
models. In this study, to extract chemical--protein interactions, we propose a
DNN-based approach incorporating uncertainty information and calibration
techniques. Our model first encodes the input sequence using a pre-trained
language-understanding model, following which it is trained using two
calibration methods: mixup training and addition of a confidence penalty loss.
Finally, the model is re-trained with augmented data that are extracted using
the estimated uncertainties. Our approach has achieved state-of-the-art
performance with regard to the Biocreative VI ChemProt task, while preserving
higher calibration abilities than those of previous approaches. Furthermore,
our approach also presents the possibilities of using uncertainty estimation
for performance improvement.
