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An emerging recipe for achieving state-of-the-art effectiveness in neuraldocument re-ranking involves utilizing large pre-trained language models -e.g., BERT - to evaluate all individual passages in the document and thenaggregating the outputs by pooling or additional Transformer layers. A majordrawback of this approach is high query latency due to the cost of evaluatingevery passage in the document with BERT. To make matters worse, this highinference cost and latency varies based on the length of the document, withlonger documents requiring more time and computation. To address thischallenge, we adopt an intra-document cascading strategy, which prunes passagesof a candidate document using a less expensive model, called ESM, beforerunning a scoring model that is more expensive and effective, called ETM. Wefound it best to train ESM (short for Efficient Student Model) via knowledgedistillation from the ETM (short for Effective Teacher Model) e.g., BERT. Thispruning allows us to only run the ETM model on a smaller set of passages whosesize does not vary by document length. Our experiments on the MS MARCO and TRECDeep Learning Track benchmarks suggest that the proposed Intra-DocumentCascaded Ranking Model (IDCM) leads to over 400% lower query latency byproviding essentially the same effectiveness as the state-of-the-art BERT-baseddocument ranking models.

Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking