Document Ranking
59 papers with code • 2 benchmarks • 6 datasets
Sort documents according to some criterion so that the "best" results appear early in the result list displayed to the user (Source: Wikipedia).
Benchmarks
These leaderboards are used to track progress in Document Ranking
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Libraries
Use these libraries to find Document Ranking models and implementations
CLUE
Qulac
Most implemented papers
XLNet: Generalized Autoregressive Pretraining for Language Understanding
zihangdai/xlnet
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NeurIPS 2019
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
stanford-futuredata/ColBERT
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ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
CEDR: Contextualized Embeddings for Document Ranking
Georgetown-IR-Lab/contextualized-reps-for-ranking
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We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
Learning deep structured semantic models for web search using clickthrough data
PaddlePaddle/PaddleRec
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CIKM 2013
The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data.
Context Attentive Document Ranking and Query Suggestion
wasiahmad/context_attentive_ir
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We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance.
Neural Vector Spaces for Unsupervised Information Retrieval
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
cxa-unique/Simplified-TinyBERT
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Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.
The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
We propose a design pattern for tackling text ranking problems, dubbed "Expando-Mono-Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different domains.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
geek-ai/irgan
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This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
Multi-Stage Document Ranking with BERT
The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing.
