  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.
