176 citations · 914 across the 61 of their papers we have counts for
10 papers · 1 filter
Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval
Bhaskar Mitra
Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challeng…
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani +1
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explic…
Semantic Product Search for Matching Structured Product Catalogs in E-Commerce
Jason Ingyu Choi, Surya Kallumadi, Bhaskar Mitra +2
Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce. Compared to web documents, product catalogs are more structured and sp…
Conformer-Kernel with Query Term Independence for Document Retrieval
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 ef…
ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search
Nick Craswell, Daniel Campos, Bhaskar Mitra +2
Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval. However, click logs have not been…
Analyzing and Learning from User Interactions for Search Clarification
Hamed Zamani, Bhaskar Mitra, Everest Chen +5
Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications…
Local Self-Attention over Long Text for Efficient Document Retrieval
Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra +2
Neural networks, particularly Transformer-based architectures, have achieved significant performance improvements on several retrieval benchmarks. When the items being retrieved ar…
On the Reliability of Test Collections for Evaluating Systems of Different Types
Emine Yilmaz, Nick Craswell, Bhaskar Mitra +1
As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their qu…
Evaluating Stochastic Rankings with Expected Exposure
Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand +2
We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for…
Overview of the TREC 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets,…