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06/07/2023
RD-Suite: A Benchmark for Ranking Distillation
The distillation of ranking models has become an important topic in both...
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05/19/2023
How Does Generative Retrieval Scale to Millions of Passages?
Popularized by the Differentiable Search Index, the emerging paradigm of...
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12/21/2022
Learning List-Level Domain-Invariant Representations for Ranking
Domain adaptation aims to transfer the knowledge acquired by models trai...
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10/12/2022
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Recently, substantial progress has been made in text ranking based on pr...
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10/11/2022
Retrieval Augmentation for T5 Re-ranker using External Sources
Retrieval augmentation has shown promising improvements in different tas...
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04/25/2022
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
State-of-the-art neural models typically encode document-query pairs usi...
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04/18/2021
Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing
Preference judgments have been demonstrated as a better alternative to g...
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04/17/2021
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
BERT-based text ranking models have dramatically advanced the state-of-t...
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09/16/2020
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
Despite the effectiveness of utilizing BERT for document ranking, the co...
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09/15/2020
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Query expansion aims to mitigate the mismatch between the language used ...
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11/21/2018
Overcoming low-utility facets for complex answer retrieval
Many questions cannot be answered simply; their answers must include num...
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10/30/2018
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performanc...
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05/02/2018
Characterizing Question Facets for Complex Answer Retrieval
Complex answer retrieval (CAR) is the process of retrieving answers to q...
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07/01/2017
An Approach for Weakly-Supervised Deep Information Retrieval
Recent developments in neural information retrieval models have been pro...
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06/30/2017
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Neural IR models, such as DRMM and PACRR, have achieved strong results b...
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06/27/2017
DE-PACRR: Exploring Layers Inside the PACRR Model
Recent neural IR models have demonstrated deep learning's utility in ad-...
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04/12/2017
