Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated. Though basic calibration methods like Monte Carlo Dropout and Ensemble can calibrate well, these methods are time-consuming in the training or inference stages. To tackle these challenges, we propose an efficient uncertainty calibration framework GPF-BERT for BERT-based conversational search, which employs a Gaussian Process layer and the focal loss on top of the BERT architecture to achieve a high-quality neural ranker. Extensive experiments are conducted to verify the effectiveness of our method. In comparison with basic calibration methods, GPF-BERT achieves the lowest empirical calibration error (ECE) in three in-domain datasets and the distributional shift tasks, while yielding the highest $R_{10}@1$ and MAP performance on most cases. In terms of time consumption, our GPF-BERT has an 8$\times$ speedup.

提出一种基于高斯过程层和焦点损失的有效不确定度校准框架GPF-BERT，用于BERT-based会话搜索，并在三个in-domain数据集和分布式转移任务上验证了其有效性。比基本的校准方法具有更低的经验校准误差（ECE），在大多数情况下具有更高的$R_{10}@1$和MAP表现，并且具有8倍的加速。

使用高斯过程进行高效的不确定性估计以实现可靠的对话响应检索