  Data-driven methods have achieved notable performance on intent detection,
which is a task to comprehend user queries. Nonetheless, they are controversial
for over-confident predictions. In some scenarios, users do not only care about
the accuracy but also the confidence of model. Unfortunately, mainstream neural
networks are poorly calibrated, with a large gap between accuracy and
confidence. To handle this problem defined as confidence calibration, we
propose a model using the hyperspherical space and rebalanced
accuracy-uncertainty loss. Specifically, we project the label vector onto
hyperspherical space uniformly to generate a dense label representation matrix,
which mitigates over-confident predictions due to overfitting sparce one-hot
label matrix. Besides, we rebalance samples of different accuracy and
uncertainty to better guide model training. Experiments on the open datasets
verify that our model outperforms the existing calibration methods and achieves
a significant improvement on the calibration metric.
