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
Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding
Most other models had poor zero-shot performance (sometimes at a random baseline level) but outstripped MaxP by as much 13-28\% after finetuning.
Document Ranking with a Pretrained Sequence-to-Sequence Model
castorini/pygaggle
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Findings of the Association for Computational Linguistics 2020
We investigate this observation further by varying target words to probe the model's use of latent knowledge.
Traditional IR rivals neural models on the MS MARCO Document Ranking Leaderboard
oaqa/FlexNeuART
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This short document describes a traditional IR system that achieved MRR@100 equal to 0. 298 on the MS MARCO Document Ranking leaderboard (on 2020-12-06).
Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits
oaqa/FlexNeuART
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We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end.
CODEC: Complex Document and Entity Collection
We also show that the manual query reformulations significantly improve document ranking and entity ranking performance.
Learning to Match Using Local and Distributed Representations of Text for Web Search
Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
AdeDZY/K-NRM
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Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.
Multi-Task Learning for Document Ranking and Query Suggestion
wasiahmad/mnsrf_ranking_suggestion
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ICLR 2018
We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search.
DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval
Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching.
Joint Optimization of Cascade Ranking Models
A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems.
