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Conversational Question Answering (ConvQA) models aim at answering a questionwith its relevant paragraph and previous question-answer pairs that occurredduring conversation multiple times. To apply such models to a real-worldscenario, some existing work uses predicted answers, instead of unavailableground-truth answers, as the conversation history for inference. However, sincethese models usually predict wrong answers, using all the predictions withoutfiltering significantly hampers the model performance. To address this problem,we propose to filter out inaccurate answers in the conversation history basedon their estimated confidences and uncertainties from the ConvQA model, withoutmaking any architectural changes. Moreover, to make the confidence anduncertainty values more reliable, we propose to further calibrate them, therebysmoothing the model predictions. We validate our models, Answer Selection-basedrealistic Conversation Question Answering, on two standard ConvQA datasets, andthe results show that our models significantly outperform relevant baselines.Code is available at: https://github.com/starsuzi/AS-ConvQA.

Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement