File size: 10,357 Bytes
3932594 1480eec 3932594 1480eec 3932594 1480eec 3932594 a1e6853 3932594 b31b0ae 3932594 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
---
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](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Uses](#uses)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data Collection and Processing](#data-collection-and-processing)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/aiana94/xMINDlarge
- **Repository:** https://github.com/andreeaiana/xMIND
- **Paper:** [MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation](https://arxiv.org/abs/2403.17876)
- **Point of Contact:** [Andreea Iana](https://andreeaiana.github.io/)
- **License:** [CC-BY-4.0-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/)
### 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](https://msnews.github.io/) dataset using open-source neural machine translation (i.e., [NLLB 3.3B](https://huggingface.co/facebook/nllb-200-3.3B)).
For the *small version* of the dataset, see [xMINDsmall](https://huggingface.co/datasets/aiana94/xMINDsmall).
### 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](https://msnews.github.io/))
- title (string): news title
- abstract (string) : news abstract (optional)
### Data Splits
For all languages, there are three split: `train`, `dev`, `test`.
## Dataset Creation
### Source Data
The news were machine-translated from the [MINDlarge dataset](https://msnews.github.io/).
#### Data Collection and Processing
We translated the news articles using the open-source model [NLLB 3.3B](https://huggingface.co/facebook/nllb-200-3.3B).
For more details regarding the translation setup and data quality, we refer to the corresponding [paper](https://arxiv.org/abs/2403.17876).
#### 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](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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](https://github.com/msnews/MIND/blob/master/MSR%20License_Data.pdf) of MIND.
### Citation Infomation
**BibTeX:**
```bibtex
@inproceedings{iana2024mind,
title={Mind your language: a multilingual dataset for cross-lingual news recommendation},
author={Iana, Andreea and Glava{\v{s}}, Goran and Paulheim, Heiko},
booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={553--563},
year={2024}
}
```
Also consider citing the following:
```bibtex
@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}
}
``` |