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

arXiv: https://arxiv.org/abs/2012.08020 · HTML: https://mlsanity.com/paper/2012.08020/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking](https://mlsanity.com/paper/2212.01340/) (2022)
- [Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits](https://mlsanity.com/paper/2102.06815/) (2021)

## Abstract (arXiv)

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). Although inferior to most BERT-based models, it outperformed several neural runs (as well as all non-neural ones), including two submissions that used a large pretrained Transformer model for re-ranking. We provide software and data to reproduce our results.