System and method for interpretable sequence and time-series data modeling

Abstract

A novel interpretable and steerable deep sequence modeling technique is disclosed. The technique combines prototype learning and RNNs to achieve both interpretability and high accuracy. Experiments and case studies on different real-world sequence prediction/classification tasks demonstrate that the model is not only as accurate as other state-of-the-art machine learning techniques but also much more interpretable. In addition, a large-scale user study on Amazon Mechanical Turk demonstrates that for familiar domains like sentiment analysis on texts, the model is able to select high quality prototypes that are well aligned with human knowledge for prediction and interpretation. Furthermore, the model obtains better interpretability without a loss of performance by incorporating the feedback from a user study to update the prototypes, demonstrating the benefits of involving human-in-the-loop for interpretable machine learning.