A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
This work addresses the problem of improving question-asking skills for users of online platforms, but it is incremental as it applies existing neural network techniques to a new dataset.
The paper tackled the problem of discriminating effective from ineffective questions by building computational models, using a dataset of over 400,000 questions from Reddit 'Ask Me Anything' threads to predict whether a question will be answered, and demonstrated the model's efficacy by comparing it with state-of-the-art baselines and human judges.
Asking effective questions is a powerful social skill. In this paper we seek to build computational models that learn to discriminate effective questions from ineffective ones. Armed with such a capability, future advanced systems can evaluate the quality of questions and provide suggestions for effective question wording. We create a large-scale, real-world dataset that contains over 400,000 questions collected from Reddit "Ask Me Anything" threads. Each thread resembles an online press conference where questions compete with each other for attention from the host. This dataset enables the development of a class of computational models for predicting whether a question will be answered. We develop a new convolutional neural network architecture with variable-length context and demonstrate the efficacy of the model by comparing it with state-of-the-art baselines and human judges.