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Free keywords:
Computer Science, Information Retrieval, cs.IR
Abstract:
We present PARADE, an end-to-end Transformer-based model that considers
document-level context for document reranking. PARADE leverages passage-level
relevance representations to predict a document relevance score, overcoming the
limitations of previous approaches that perform inference on passages
independently. Experiments on two ad-hoc retrieval benchmarks demonstrate
PARADE's effectiveness over such methods. We conduct extensive analyses on
PARADE's efficiency, highlighting several strategies for improving it. When
combined with knowledge distillation, a PARADE model with 72\% fewer parameters
achieves effectiveness competitive with previous approaches using BERT-Base.
Our code is available at \url{https://github.com/canjiali/PARADE}.
