117 citations · 284 across the 17 of their papers we have counts for
26 papers
Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?
Gabriella Kazai, Bhaskar Mitra, Anlei Dong +2
Traditional information retrieval (IR) ranking models process the full text of documents. Newer models based on Transformers, however, would incur a high computational cost when pr…
Neural Approaches to Conversational Information Retrieval
Jianfeng Gao, Chenyan Xiong, Paul Bennett +1
A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface which allows users to interact with the system to seek i…
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…