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Found 6 papers, 2 papers with code
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently.
L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations
First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection.
Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models
When sampling top-ranked results (excluding the labeled positives) as negatives from a stronger retriever, the performance of the learned NRM becomes even worse.
Pre-training Methods in Information Retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need.
A Discriminative Semantic Ranker for Question Retrieval
However, these methods often lose the discriminative power as term-based methods, thus introduce noise during retrieval and hurt the recall performance.
Semantic Models for the First-stage Retrieval: A Comprehensive Review
We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development.
