license: cc-by-nc-sa-4.0
task_categories:
- translation
- text-retrieval
language:
- fi
- gn
- ht
- id
- ja
- ka
- ro
- so
- sw
- ta
- th
- tr
- vi
- zh
tags:
- news
- multilingual
- machine-translated
- nllb
pretty_name: xMINDlarge
size_categories:
- 10K<n<100K
multilinguality:
- translation
- multilingual
- multi-parallel
source_datasets:
- mind
configs:
- config_name: fin
data_files:
- split: train
path: data/fin/train.parquet.gzip
- split: dev
path: data/fin/dev.parquet.gzip
- split: test
path: data/fin/test.parquet.gzip
- config_name: grn
data_files:
- split: train
path: data/grn/train.parquet.gzip
- split: dev
path: data/grn/dev.parquet.gzip
- split: test
path: data/grn/test.parquet.gzip
- config_name: hat
data_files:
- split: train
path: data/hat/train.parquet.gzip
- split: dev
path: data/hat/dev.parquet.gzip
- split: test
path: data/hat/test.parquet.gzip
- config_name: ind
data_files:
- split: train
path: data/ind/train.parquet.gzip
- split: dev
path: data/ind/dev.parquet.gzip
- split: test
path: data/ind/test.parquet.gzip
- config_name: jpn
data_files:
- split: train
path: data/jpn/train.parquet.gzip
- split: dev
path: data/jpn/dev.parquet.gzip
- split: test
path: data/jpn/test.parquet.gzip
- config_name: kat
data_files:
- split: train
path: data/kat/train.parquet.gzip
- split: dev
path: data/kat/dev.parquet.gzip
- split: test
path: data/kat/test.parquet.gzip
- config_name: ron
data_files:
- split: train
path: data/ron/train.parquet.gzip
- split: dev
path: data/ron/dev.parquet.gzip
- split: test
path: data/ron/test.parquet.gzip
- config_name: som
data_files:
- split: train
path: data/som/train.parquet.gzip
- split: dev
path: data/som/dev.parquet.gzip
- split: test
path: data/som/test.parquet.gzip
- config_name: swh
data_files:
- split: train
path: data/swh/train.parquet.gzip
- split: dev
path: data/swh/dev.parquet.gzip
- split: test
path: data/swh/test.parquet.gzip
- config_name: tam
data_files:
- split: train
path: data/tam/train.parquet.gzip
- split: dev
path: data/tam/dev.parquet.gzip
- split: test
path: data/tam/test.parquet.gzip
- config_name: tha
data_files:
- split: train
path: data/tha/train.parquet.gzip
- split: dev
path: data/tha/dev.parquet.gzip
- split: test
path: data/tha/test.parquet.gzip
- config_name: tur
data_files:
- split: train
path: data/tur/train.parquet.gzip
- split: dev
path: data/tur/dev.parquet.gzip
- split: test
path: data/tur/test.parquet.gzip
- config_name: vie
data_files:
- split: train
path: data/vie/train.parquet.gzip
- split: dev
path: data/vie/dev.parquet.gzip
- split: test
path: data/vie/test.parquet.gzip
- config_name: zho
data_files:
- split: train
path: data/zho/train.parquet.gzip
- split: dev
path: data/zho/dev.parquet.gzip
- split: test
path: data/zho/test.parquet.gzip
Dataset Card for xMINDlarge
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://huggingface.co/datasets/aiana94/polynews
- Repository: https://github.com/andreeaiana/xMIND
- Paper: MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation
- Point of Contact: Andreea Iana
- License: CC-BY-4.0-NC-SA
Dataset Summary
xMINDlarge is an open, large-scale multi-parallel news dataset for multi- and cross-lingual news recommendation. It is derived from the English MINDlarge dataset using open-source neural machine translation (i.e., NLLB 3.3B).
Uses
This dataset can be used for machine translation, text retrieval, or as a benchmark dataset for news recommendation.
Languages
xMIND contains news translated into 14 linguistically and geographically diverse languages, with digital footprints of varying sizes.
| Code | Language | Script | Macro-area | Family | Genus |
|---|---|---|---|---|---|
| FIN | Finnish | Latin | Eurasia | Uralic | Finnic |
| GRN | Guarani | Latin | South-America | Tupian | Maweti-Guarani |
| HAT | Haitian Creole | Latin | North-America | Indo-European | Creoles and Pidgins |
| IND | Indonesian | Latin | Papunesia | Austronesian | Malayo-Sumbawan |
| JPN | Japanese | Japanese | Eurasia | Japonic | Japanesic |
| KAT | Georgian | Georgian | Eurasia | Kartvelic | Georgian-Zan |
| RON | Romanian | Latin | Eurasia | Indo-European | Romance |
| SOM | Somali | Latin | Africa | Afro-Asiatic | Lowland East Cushitic |
| SWH | Swahili | Latin | Africa | Niger-Congo | Bantu |
| TAM | Tamil | Tamil | Eurasia | Dravidian | Dravidian |
| THA | Thai | Thai | Eurasia | Tai-Kadai | Kam-Tai |
| TUR | Turkish | Latin | Eurasia | Altaic | Turkic |
| VIE | Vietnamese | Latin | Eurasia | Austro-Asiatic | Vietic |
| ZHO | Mandarin Chinese | Han | Eurasia | Sino-Tibetan | Sinitic |
Dataset Structure
Data Instances
>>> from datasets import load_dataset
>>> data = load_dataset('aiana94/xMINDlarge', 'ron')
# Please, specify the language code.
# A data point example is below:
{
"nid": "N49265"
"title": "Aceste reţete cu sos de afine sunt perfecte pentru cina de Ziua Recunoştinţei.",
"abstract": "Nu vei mai vrea niciodată versiunea cumpărată din magazin."
}
Data Fields
- nid (string): news ID (same as in the MIND dataset)
- title (string): news title
- abstract (string) : news abstract (optional)
Data Splits
For all languages, there are three split: , dev, test.
Dataset Creation
Source Data
The news were machine-translated from the MINDlarge dataset.
Data Collection and Processing
We translated the news articles using the open-source model NLLB 3.3B. For more details regarding the translation setup and data quality, we refer to the corresponding paper.
Personal and Sensitive Information
The data is sourced from newspaper sources and contains mentions of public figures and individuals.
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
Users should keep in mind that the dataset contains short news texts (e.g., news titles and abstracts), which might limit the applicability of the developed systems to other domains.
Additional Information
Licensing Information
The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. If you intend to use, adapt, or share xMINDlarge, particularly together with additional news and click behavior information from the original MIND dataset, please read and reference the Microsoft Research License Terms of MIND.
Citation Infomation
BibTeX:
@misc{iana2024mind,
title={MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation},
author={Andreea Iana and Goran Glavaš and Heiko Paulheim},
year={2024},
eprint={2403.17876},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
Also consider citing the following:
@inproceedings{wu2020mind,
title={Mind: A large-scale dataset for news recommendation},
author={Wu, Fangzhao and Qiao, Ying and Chen, Jiun-Hung and Wu, Chuhan and Qi, Tao and Lian, Jianxun and Liu, Danyang and Xie, Xing and Gao, Jianfeng and Wu, Winnie and others},
booktitle={Proceedings of the 58th annual meeting of the association for computational linguistics},
pages={3597--3606},
year={2020}
}