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

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

15 Dec 2020arXiv:2012.08020archive 2025-07-28

Leonid Boytsov

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.

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oaqa/FlexNeuART officialmentioned in papermentioned on GitHubpytorch report

oaqa/knn4qa mentioned on GitHubpytorch report

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Document Ranking Re-Ranking

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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