Liu, Fei



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

AAAI Conferences

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.


Modeling Language Vagueness in Privacy Policies Using Deep Neural Networks

AAAI Conferences

Website privacy policies are too long to read and difficult to understand. The over-sophisticated language undermines the effectiveness of privacy notices. People become less willing to share their personal information when they perceive the privacy policy as vague. The goal of this paper is to decode vagueness from a natural language processing perspective. While thoroughly identifying the vague terms and their linguistic scope remains an elusive challenge, in this work we seek to learn vector representations of words in privacy policies using deep neural networks. The vector representations are fed to an interactive visualization tool (LSTMVis) to test on their ability to discover syntactically and semantically related terms. The approach holds promise for modeling and understanding language vagueness.