Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval

• URL: http://arxiv.org/abs/2303.08599v1
• Date: Wed, 15 Mar 2023 13:12:16 GMT
• Title: Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval
• Authors: Tong Ye, Zhitao Li, Jianzong Wang, Ning Cheng, Jing Xiao
• Abstract summary: We propose an efficient uncertainty calibration framework GPF-BERT for BERT-based conversational search. In comparison with basic calibration methods, GPF-BERT achieves the lowest empirical calibration error (ECE) in three in-domain datasets. In terms of time consumption, our GPF-BERT has an 8$times$ speedup.
• Score: 31.32746943236811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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