117 citations · 587 across the 36 of their papers we have counts for
6 papers · 1 filter
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani +2
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e.g., BERT - to evaluate all in…
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big ques…
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +3
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available. Results so far indicate that…
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani +1
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less e…
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard
Jimmy Lin, Daniel Campos, Nick Craswell +2
Leaderboards are a ubiquitous part of modern research in applied machine learning. By design, they sort entries into some linear order, where the top-scoring entry is recognized as…
Overview of the TREC 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +1
This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime. We again have a document retrieval task and a p…