Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times. To apply such models to a real-world scenario, some existing work uses predicted answers, instead of unavailable ground-truth answers, as the conversation history for inference. However, since these models usually predict wrong answers, using all the predictions without filtering significantly hampers the model performance. To address this problem, we propose to filter out inaccurate answers in the conversation history based on their estimated confidences and uncertainties from the ConvQA model, without making any architectural changes. Moreover, to make the confidence and uncertainty values more reliable, we propose to further calibrate them, thereby smoothing the model predictions. We validate our models, Answer Selection-based realistic Conversation Question Answering, on two standard ConvQA datasets, and the results show that our models significantly outperform relevant baselines. Code is available at: https://github.com/starsuzi/AS-ConvQA.

翻译：对话问答（ConvQA）模型旨在结合相关段落及对话过程中多次出现的历史问答对来回答问题。为将该类模型应用于实际场景，现有部分工作采用模型预测的答案（而非无法获取的真实答案）作为对话历史进行推理。然而，由于这些模型通常会产生错误预测，若不加筛选地使用所有预测结果，将严重损害模型性能。针对该问题，我们提出在不改变模型架构的前提下，基于ConvQA模型对预测答案的置信度与不确定性估计值，过滤对话历史中的不准确答案。此外，为使置信度与不确定性值更可靠，我们进一步对其执行校准操作，从而平滑模型预测结果。我们在两个标准ConvQA数据集上验证了所提模型——基于答案选择的现实对话问答，结果表明该模型显著优于相关基线方法。代码开源地址：https://github.com/starsuzi/AS-ConvQA。