27 citations · 113 across the 17 of their papers we have counts for
3 papers · 1 filter
Debiasing Neural Retrieval via In-batch Balancing Regularization
Yuantong Li, Xiaokai Wei, Zijian Wang +4
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair rankin…
Contrastive Fine-tuning Improves Robustness for Neural Rankers
Xiaofei Ma, Cicero Nogueira dos Santos, Andrew O. Arnold
The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain. In this paper, we present a novel method f…
Neural document expansion for ad-hoc information retrieval
Cheng Tang, Andrew Arnold
Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. Howeve…