A Study Of Question Effectiveness Using Reddit "Ask Me Anything" Threads

Abstract

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.

Publication Date

1-1-2017

Publication Title

FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference

Number of Pages

26-31

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85029515568 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85029515568

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