  Recent innovations in Transformer-based ranking models have advanced the
state-of-the-art in information retrieval. However, these Transformers are
computationally expensive, and their opaque hidden states make it hard to
understand the ranking process. In this work, we modularize the Transformer
ranker into separate modules for text representation and interaction. We show
how this design enables substantially faster ranking using offline pre-computed
representations and light-weight online interactions. The modular design is
also easier to interpret and sheds light on the ranking process in Transformer
rankers.
