  This paper describes our participation to the 2022 TREC Deep Learning
challenge. We submitted runs to all four tasks, with a focus on the full
retrieval passage task. The strategy is almost the same as 2021, with first
stage retrieval being based around SPLADE, with some added ensembling with
ColBERTv2 and DocT5. We also use the same strategy of last year for the second
stage, with an ensemble of re-rankers trained using hard negatives selected by
SPLADE. Initial result analysis show that the strategy is still strong, but is
still unclear to us what next steps should we take.
