  The Federal Reserve System (the Fed) plays a significant role in affecting
monetary policy and financial conditions worldwide. Although it is important to
analyse the Fed's communications to extract useful information, it is generally
long-form and complex due to the ambiguous and esoteric nature of content. In
this paper, we present FedNLP, an interpretable multi-component Natural
Language Processing system to decode Federal Reserve communications. This
system is designed for end-users to explore how NLP techniques can assist their
holistic understanding of the Fed's communications with NO coding. Behind the
scenes, FedNLP uses multiple NLP models from traditional machine learning
algorithms to deep neural network architectures in each downstream task. The
demonstration shows multiple results at once including sentiment analysis,
summary of the document, prediction of the Federal Funds Rate movement and
visualization for interpreting the prediction model's result.
