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Although deep learning models have proven effective at solving problems innatural language processing, the mechanism by which they come to theirconclusions is often unclear. As a result, these models are generally treatedas black boxes, yielding no insight of the underlying learned patterns. In thispaper we consider Long Short Term Memory networks (LSTMs) and demonstrate a newapproach for tracking the importance of a given input to the LSTM for a givenoutput. By identifying consistently important patterns of words, we are able todistill state of the art LSTMs on sentiment analysis and question answeringinto a set of representative phrases. This representation is thenquantitatively validated by using the extracted phrases to construct a simple,rule-based classifier which approximates the output of the LSTM.

Automatic Rule Extraction from Long Short Term Memory Networks