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09/19/2023
Large language models can accurately predict searcher preferences
Relevance labels, which indicate whether a search result is valuable to ...
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04/25/2023
Patterns of gender-specializing query reformulation
Users of search systems often reformulate their queries by adding query ...
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01/30/2023
Zero-shot Clarifying Question Generation for Conversational Search
A long-standing challenge for search and conversational assistants is qu...
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06/26/2022
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems
Recently, several dense retrieval (DR) models have demonstrated competit...
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04/21/2022
Human Preferences as Dueling Bandits
The dramatic improvements in core information retrieval tasks engendered...
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01/21/2022
Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?
Traditional information retrieval (IR) ranking models process the full t...
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01/13/2022
Neural Approaches to Conversational Information Retrieval
A conversational information retrieval (CIR) system is an information re...
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05/20/2021
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
An emerging recipe for achieving state-of-the-art effectiveness in neura...
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05/09/2021
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public ...
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04/19/2021
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
The TREC Deep Learning (DL) Track studies ad hoc search in the large dat...
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04/19/2021
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence
The Transformer-Kernel (TK) model has demonstrated strong reranking perf...
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02/25/2021
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard
Leaderboards are a ubiquitous part of modern research in applied machine...
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02/15/2021
Overview of the TREC 2020 deep learning track
This is the second year of the TREC Deep Learning Track, with the goal o...
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11/14/2020
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
We benchmark Conformer-Kernel models under the strict blind evaluation s...
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07/20/2020
Conformer-Kernel with Query Term Independence for Document Retrieval
The Transformer-Kernel (TK) model has demonstrated strong reranking perf...
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07/17/2020
Scalable Methods for Calculating Term Co-Occurrence Frequencies
Search techniques make use of elementary information such as term freque...
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06/17/2020
MIMICS: A Large-Scale Data Collection for Search Clarification
Search clarification has recently attracted much attention due to its ap...
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06/09/2020
ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search
Users of Web search engines reveal their information needs through queri...
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05/30/2020
Analyzing and Learning from User Interactions for Search Clarification
Asking clarifying questions in response to search queries has been recog...
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05/11/2020
Local Self-Attention over Long Text for Efficient Document Retrieval
Neural networks, particularly Transformer-based architectures, have achi...
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04/28/2020
On the Reliability of Test Collections for Evaluating Systems of Different Types
As deep learning based models are increasingly being used for informatio...
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03/17/2020
Overview of the TREC 2019 deep learning track
The Deep Learning Track is a new track for TREC 2019, with the goal of s...
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12/18/2019
Macaw: An Extensible Conversational Information Seeking Platform
Conversational information seeking (CIS) has been recognized as a major ...
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12/10/2019
Duet at TREC 2019 Deep Learning Track
This report discusses three submissions based on the Duet architecture t...
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07/24/2019
Generic Intent Representation in Web Search
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a ...
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07/08/2019
Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks
Classical information retrieval (IR) methods, such as query likelihood a...
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04/15/2019
An Axiomatic Approach to Regularizing Neural Ranking Models
Axiomatic information retrieval (IR) seeks a set of principle properties...
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03/18/2019
An Updated Duet Model for Passage Re-ranking
We propose several small modifications to Duet---a deep neural ranking m...
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11/25/2017
Neural Ranking Models with Multiple Document Fields
Deep neural networks have recently shown promise in the ad-hoc retrieval...
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05/25/2016
