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.

提出一种使用超球空间和重新平衡的准确性-不确定性损失来解决置信度校准问题的模型，通过在超球空间上投影标签向量来生成密集的标签表示矩阵，同时对不同准确性和不确定性的样本进行重新平衡来更好地指导模型训练，实验表明该模型优于现有校准方法，并在校准指标上取得了显著提高。

利用超球空间和重新平衡的精度-不确定性损失进行意图检测置信度校准