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Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022
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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.
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Cited by 1 Pith paper
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Reranking with Compressed Document Representation
A reranker trained on 8-token PISCO document embeddings plus a short query achieves near-identical nDCG@10 to full-text rerankers on BeIR and TREC-DL while running up to 16x faster.
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