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Sort documents according to some criterion so that the "best" results appear early in the result list displayed to the user (Source: Wikipedia).

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TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Datasets

Latest papers without code

Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence

19 Apr 2021

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less effective) alternative to other Transformer-based architectures that employ (i) large-scale pretraining (high training cost), (ii) joint encoding of query and document (high inference cost), and (iii) larger number of Transformer layers (both high training and high inference costs).

DOCUMENT RANKING

An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking

30 Mar 2021

Recently introduced pre-trained contextualized autoregressive models like BERT have shown improvements in document retrieval tasks.

DOCUMENT RANKING

A Neural Passage Model for Ad-hoc Document Retrieval

16 Mar 2021

Traditional statistical retrieval models often treat each document as a whole.

DOCUMENT RANKING

Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation

11 Mar 2021

When monoBERT is used as the cross-encoder teacher, together with either TwinBERT or ColBERT as the bi-encoder teacher, TRMD produces a student bi-encoder that performs better than the corresponding baseline bi-encoder.

DOCUMENT RANKING

LRG at TREC 2020: Document Ranking with XLNet-Based Models

28 Feb 2021

We experiment with two hybrid models which first filter out the best podcasts based on user's query with a classical IR technique, and then perform re-ranking on the shortlisted documents based on the detailed description using a transformer-based model.

DOCUMENT RANKING INFORMATION RETRIEVAL RE-RANKING

OpenMatch: An Open Source Library for Neu-IR Research

30 Jan 2021

OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research.

DOCUMENT RANKING INFORMATION RETRIEVAL

The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models

14 Jan 2021

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.

DOCUMENT RANKING

Traditional IR rivals neural models on the MS MARCO Document Ranking Leaderboard

15 Dec 2020

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).

DOCUMENT RANKING RE-RANKING

Non-Linear Multiple Field Interactions Neural Document Ranking

18 Nov 2020

Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page.

DOCUMENT RANKING