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PARADE: Passage Representation Aggregation for Document Reranking

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Yates,  Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:2008.09093.pdf
(Preprint), 2MB

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Citation

Li, C., Yates, A., MacAvaney, S., He, B., & Sun, Y. (2020). PARADE: Passage Representation Aggregation for Document Reranking. Retrieved from https://arxiv.org/abs/2008.09093.


Cite as: http://hdl.handle.net/21.11116/0000-0008-06CF-9
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}.