Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Sudha Rao, Hal Daumé · 2018
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.