activity
20162025
most citedOverview of the TREC 2020 deep learning track

117 citations · 304 across the 25 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.IR2019

Report on the First HIPstIR Workshop on the Future of Information Retrieval

Laura Dietz, Bhaskar Mitra, Jeremy Pickens +21

The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first ite…

cs.IR20191 cited

Duet at TREC 2019 Deep Learning Track

Bhaskar Mitra, Nick Craswell

This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a…

cs.IR201921 cited

Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks

Bhaskar Mitra, Corby Rosset, David Hawking +3

Classical information retrieval (IR) methods, such as query likelihood and BM25, score documents independently w.r.t. each query term, and then accumulate the scores. Assuming quer…

cs.IR2019

An Axiomatic Approach to Regularizing Neural Ranking Models

Corby Rosset, Bhaskar Mitra, Chenyan Xiong +3

Axiomatic information retrieval (IR) seeks a set of principle properties desirable in IR models. These properties when formally expressed provide guidance in the search for better…

cs.IR201934 cited

An Updated Duet Model for Passage Re-ranking

Bhaskar Mitra, Nick Craswell

We propose several small modifications to Duet---a deep neural ranking model---and evaluate the updated model on the MS MARCO passage ranking task. We report significant improvemen…