Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information

ACL 2018

Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information

Jan 28, 2021
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Abstract: Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines. Authors: Sudha Rao, Hal Daumé III (University of Maryland)

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