40 citations · 101 across the 19 of their papers we have counts for
4 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…
Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification
Mingwen Dong, Christos Christodoulopoulos, Sheng-Min Shih +1
Accurate evidence retrieval is essential for automated fact checking. Little previous research has focused on the differences between true and false claims and how they affect evid…
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…
Passage Ranking with Weak Supervision
Peng Xu, Xiaofei Ma, Ramesh Nallapati +1
In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \citep{Ratner2016}, which enables us to leverage mul…