Appeared in: arXiv
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.
BibTeX @article{li:arxiv2020-parade, author = {Li, Canjia and Yates, Andrew and MacAvaney, Sean and He, Ben and Sun, Yingfei}, title = {PARADE: Passage Representation Aggregation for Document Reranking}, year = {2020}, url = {https://arxiv.org/abs/2008.09093v2}, journal = {arXiv}, volume = {abs/2008.09093} }