103 citations · 123 across the 5 of their papers we have counts for
8 papers
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…
Distilling Interpretable Models into Human-Readable Code
Walker Ravina, Ethan Sterling, Olexiy Oryeshko +5
The goal of model distillation is to faithfully transfer teacher model knowledge to a model which is faster, more generalizable, more interpretable, or possesses other desirable ch…
Interpretable Learning-to-Rank with Generalized Additive Models
Honglei Zhuang, Xuanhui Wang, Michael Bendersky +7
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area. Recent progress on interpretable ranking models largely focuses on generating…
Learning-to-Rank with BERT in TF-Ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky +1
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank…
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky +1
How to leverage cross-document interactions to improve ranking performance is an important topic in information retrieval (IR) research. However, this topic has not been well-studi…
Estimating Position Bias without Intrusive Interventions
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang +3
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counter…