PARADE: Passage Representation Aggregation for Document Reranking

Understand

Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking.

• Due to inherent sequence length limits of these models, they need to be run over a document's passages, rather than processing the entire document sequence at once.
• Although several approaches for aggregating passage-level signals have been proposed, there has yet to be an extensive comparison of these techniques.
• In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.