Interpretation of Prediction Models Using the Input Gradient
This addresses the need for interpretability in machine learning models, particularly for users in fields like NLP, though it appears incremental as it builds on gradient-based techniques.
The paper tackles the problem of interpreting complex 'black box' predictive models by proposing a method that uses partial derivatives of the model with respect to the input, applied to convolutional and multi-layer neural networks in natural language processing.
State of the art machine learning algorithms are highly optimized to provide the optimal prediction possible, naturally resulting in complex models. While these models often outperform simpler more interpretable models by order of magnitudes, in terms of understanding the way the model functions, we are often facing a "black box". In this paper we suggest a simple method to interpret the behavior of any predictive model, both for regression and classification. Given a particular model, the information required to interpret it can be obtained by studying the partial derivatives of the model with respect to the input. We exemplify this insight by interpreting convolutional and multi-layer neural networks in the field of natural language processing.