  Popular QA benchmarks like SQuAD have driven progress on the task of
identifying answer spans within a specific passage, with models now surpassing
human performance. However, retrieving relevant answers from a huge corpus of
documents is still a challenging problem, and places different requirements on
the model architecture. There is growing interest in developing scalable answer
retrieval models trained end-to-end, bypassing the typical document retrieval
step. In this paper, we introduce Retrieval Question-Answering (ReQA), a
benchmark for evaluating large-scale sentence-level answer retrieval models. We
establish baselines using both neural encoding models as well as classical
information retrieval techniques. We release our evaluation code to encourage
further work on this challenging task.
