most citedP^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning

14 citations · 14 across the 1 of their papers we have counts for

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1 paper

cs.IR2022★ 14 cited

P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning

Xiaomeng Hu, Shi Yu, Chenyan Xiong +3

Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and st…