  Given BM25's enduring competitiveness as an information retrieval baseline,
we investigate to what extent it can be even further improved by augmenting and
re-weighting its sparse query-vector representation. We propose an approach to
learning an augmentation and a re-weighting end-to-end, and we find that our
approach improves performance over BM25 while retaining its speed. We
furthermore find that the learned augmentations and re-weightings transfer well
to unseen datasets.
