54 citations · 96 across the 14 of their papers we have counts for
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cs.IR2020
Pretrained Transformers for Text Ranking: BERT and Beyond
Jimmy Lin, Rodrigo Nogueira, Andrew Yates
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, ins…
cs.IR2020
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Zhi Zheng, Kai Hui, Ben He +3
Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant info…
cs.IR2020
PARADE: Passage Representation Aggregation for Document Reranking
Canjia Li, Andrew Yates, Sean MacAvaney +2
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…