AI-driven medical history-taking is an important component in symptom
checking, automated patient intake, triage, and other AI virtual care
applications. As history-taking is extremely varied, machine learning models
require a significant amount of data to train. To overcome this challenge,
existing systems are developed using indirect data or expert knowledge. This
leads to a training-inference gap as models are trained on different kinds of
data than what they observe at inference time. In this work, we present a
two-stage re-ranking approach that helps close the training-inference gap by
re-ranking the first-stage question candidates using a dialogue-contextualized
model. For this, we propose a new model, global re-ranker, which cross-encodes
the dialogue with all questions simultaneously, and compare it with several
existing neural baselines. We test both transformer and S4-based language model
backbones. We find that relative to the expert system, the best performance is
achieved by our proposed global re-ranker with a transformer backbone,
resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77%
higher mean average precision (mAP).

本文提出了一种两阶段的重新排序方法，通过使用对话上下文模型重新排序第一阶段问题的候选项来帮助弥合训练 - 推断差距，从而解决了由于现有系统使用间接数据或专家知识开发而导致的模型训练 - 推理鸿沟问题，并发现相对于专家系统，我们所提出的全局重新排序器结合转换器骨干的最佳性能，导致 30％更高的标准化折扣累积增益（nDCG）和 77％更高的平均精度（mAP）。