  This paper describes our participation in the 2022 TREC NeuCLIR challenge. We
submitted runs to two out of the three languages (Farsi and Russian), with a
focus on first-stage rankers and comparing mono-lingual strategies to Adhoc
ones. For monolingual runs, we start from pretraining models on the target
language using MLM+FLOPS and then finetuning using the MSMARCO translated to
the language either with ColBERT or SPLADE as the retrieval model. While for
the Adhoc task, we test both query translation (to the target language) and
back-translation of the documents (to English). Initial result analysis shows
that the monolingual strategy is strong, but that for the moment Adhoc achieved
the best results, with back-translating documents being better than translating
queries.
