This link gathers 72 noisy versions of the MS-Marco-Passage Ranking dataset consisting of three noise types (insertion, deletion, substitution), two different distributions of errors in the text (Batch 1 where errors are distributed in few words in the text and Batch2 where errors are more evenly spread out between words) and 12 different intensities of noise (CER varying from 3% to 36% with intervals of 3%).

The exact dataset that has been used is the MS-Marco-passagetest2020-top1000. The original dataset is available at https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-passagetest2020-top1000.tsv.gz.

This dataset has been built using the test set of the dataset MS MARCO Passage ranking from the paper @article{bajaj2016ms,
title={Ms marco: A human generated machine reading comprehension dataset},
author={Bajaj, Payal and Campos, Daniel and Craswell, Nick and Deng, Li and Gao, Jianfeng and Liu, Xiaodong and Majumder, Rangan and McNamara, Andrew and Mitra, Bhaskar and Nguyen, Tri and others},
journal={arXiv preprint arXiv:1611.09268},
year={2016}
}.

This dataset has been built using the library nlpaug (https://github.com/makcedward/nlpaug) to inject noise augmentations in the dataset's text.
license:cc-by-4.0 region:us
{"license": "cc-by-4.0"}
TAGS
#license-cc-by-4.0 #region-us 

This link gathers 72 noisy versions of the MS-Marco-Passage Ranking dataset consisting of three noise types (insertion, deletion, substitution), two different distributions of errors in the text (Batch 1 where errors are distributed in few words in the text and Batch2 where errors are more evenly spread out between words) and 12 different intensities of noise (CER varying from 3% to 36% with intervals of 3%).

The exact dataset that has been used is the MS-Marco-passagetest2020-top1000. The original dataset is available at URL

This dataset has been built using the test set of the dataset MS MARCO Passage ranking from the paper @article{bajaj2016ms,
title={Ms marco: A human generated machine reading comprehension dataset},
author={Bajaj, Payal and Campos, Daniel and Craswell, Nick and Deng, Li and Gao, Jianfeng and Liu, Xiaodong and Majumder, Rangan and McNamara, Andrew and Mitra, Bhaskar and Nguyen, Tri and others},
journal={arXiv preprint arXiv:1611.09268},
year={2016}
}.

This dataset has been built using the library nlpaug (URL to inject noise augmentations in the dataset's text.
TAGS
#license-cc-by-4.0 #region-us 

passage: TAGS
#license-cc-by-4.0 #region-us 
