24 citations · 99 across the 22 of their papers we have counts for
5 papers · 1 filter
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Yunan Zhang, Le Yan, Zhen Qin +5
Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention rec…
Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian, Honglei Zhuang, Zhen Qin +7
Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning,…
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
Aijun Bai, Rolf Jagerman, Zhen Qin +6
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score…
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman +6
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful s…
Retrieval Augmentation for T5 Re-ranker using External Sources
Kai Hui, Tao Chen, Zhen Qin +4
Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We…