  Retrieval models in information retrieval are used to rank documents for
typically under-specified queries. Today machine learning is used to learn
retrieval models from click logs and/or relevance judgments that maximizes an
objective correlated with user satisfaction. As these models become
increasingly powerful and sophisticated, they also become harder to understand.
Consequently, it is hard for to identify artifacts in training, data specific
biases and intents from a complex trained model like neural rankers even if
trained purely on text features. EXS is a search system designed specifically
to provide its users with insight into the following questions: `What is the
intent of the query according to the ranker?', `Why is this document ranked
higher than another?' and `Why is this document relevant to the query?'. EXS
uses a version of a popular posthoc explanation method for classifiers -- LIME,
adapted specifically to answer these questions. We show how such a system can
effectively help a user understand the results of neural rankers and highlight
areas of improvement.
