Datasets:

Multilinguality:
multilingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
File size: 11,893 Bytes
5186455
 
 
 
 
123b162
609538f
 
 
 
 
 
5db1519
5186455
 
 
d76b750
5186455
 
 
7529314
 
aadd8ae
6b7d2c1
99a0348
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c1e8296
 
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
c1e8296
 
 
 
 
 
 
 
 
 
99a0348
c1e8296
99a0348
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5186455
 
5db1519
5186455
 
 
 
aadd8ae
5186455
 
 
aadd8ae
 
5186455
 
 
 
 
 
 
 
 
 
 
 
 
02083a3
5186455
 
 
7ed59db
5db1519
 
 
 
5186455
 
 
5db1519
 
5186455
5db1519
5186455
 
 
 
 
 
 
5db1519
5186455
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5db1519
 
 
 
 
 
 
5186455
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
02083a3
 
 
c1e8296
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
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- en
- es
- fr
- ru
- zh
license: other
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: united-nations-parallel-corpus
pretty_name: United Nations Parallel Corpus
config_names:
- ar-en
- ar-es
- ar-fr
- ar-ru
- ar-zh
- en-es
- en-fr
- en-ru
- en-zh
- es-fr
- es-ru
- es-zh
- fr-ru
- fr-zh
- ru-zh
dataset_info:
- config_name: ar-en
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - ar
        - en
  splits:
  - name: train
    num_bytes: 8039673899
    num_examples: 20044478
  download_size: 3638378262
  dataset_size: 8039673899
- config_name: ar-es
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - ar
        - es
  splits:
  - name: train
    num_bytes: 8715738416
    num_examples: 20532014
  download_size: 3938780664
  dataset_size: 8715738416
- config_name: ar-fr
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - ar
        - fr
  splits:
  - name: train
    num_bytes: 8897831806
    num_examples: 20281645
  download_size: 3976788621
  dataset_size: 8897831806
- config_name: ar-ru
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - ar
        - ru
  splits:
  - name: train
    num_bytes: 11395906619
    num_examples: 20571334
  download_size: 4836152717
  dataset_size: 11395906619
- config_name: ar-zh
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - ar
        - zh
  splits:
  - name: train
    num_bytes: 6447644160
    num_examples: 17306056
  download_size: 3050491574
  dataset_size: 6447644160
- config_name: en-es
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - en
        - es
  splits:
  - name: train
    num_bytes: 8241615138
    num_examples: 25227004
  download_size: 3986062875
  dataset_size: 8241615138
- config_name: en-fr
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - en
        - fr
  splits:
  - name: train
    num_bytes: 9718498495
    num_examples: 30340652
  download_size: 4580188433
  dataset_size: 9718498495
- config_name: en-ru
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - en
        - ru
  splits:
  - name: train
    num_bytes: 11156144547
    num_examples: 25173398
  download_size: 4899993315
  dataset_size: 11156144547
- config_name: en-zh
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - en
        - zh
  splits:
  - name: train
    num_bytes: 4988798590
    num_examples: 17451549
  download_size: 2554362693
  dataset_size: 4988798590
- config_name: es-fr
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - es
        - fr
  splits:
  - name: train
    num_bytes: 9230870495
    num_examples: 25887160
  download_size: 4379207947
  dataset_size: 9230870495
- config_name: es-ru
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - es
        - ru
  splits:
  - name: train
    num_bytes: 10789762294
    num_examples: 22294106
  download_size: 4748706797
  dataset_size: 10789762294
- config_name: es-zh
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - es
        - zh
  splits:
  - name: train
    num_bytes: 5475351906
    num_examples: 17599223
  download_size: 2774470102
  dataset_size: 5475351906
- config_name: fr-ru
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - fr
        - ru
  splits:
  - name: train
    num_bytes: 12099649535
    num_examples: 25219973
  download_size: 5264326148
  dataset_size: 12099649535
- config_name: fr-zh
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - fr
        - zh
  splits:
  - name: train
    num_bytes: 5679208110
    num_examples: 17521170
  download_size: 2828146104
  dataset_size: 5679208110
- config_name: ru-zh
  features:
  - name: translation
    dtype:
      translation:
        languages:
        - ru
        - zh
  splits:
  - name: train
    num_bytes: 7905429097
    num_examples: 17920922
  download_size: 3601589709
  dataset_size: 7905429097
configs:
- config_name: ar-en
  data_files:
  - split: train
    path: ar-en/train-*
- config_name: ar-es
  data_files:
  - split: train
    path: ar-es/train-*
- config_name: ar-fr
  data_files:
  - split: train
    path: ar-fr/train-*
- config_name: ar-ru
  data_files:
  - split: train
    path: ar-ru/train-*
- config_name: ar-zh
  data_files:
  - split: train
    path: ar-zh/train-*
- config_name: en-es
  data_files:
  - split: train
    path: en-es/train-*
- config_name: en-fr
  data_files:
  - split: train
    path: en-fr/train-*
- config_name: en-ru
  data_files:
  - split: train
    path: en-ru/train-*
- config_name: en-zh
  data_files:
  - split: train
    path: en-zh/train-*
- config_name: es-fr
  data_files:
  - split: train
    path: es-fr/train-*
- config_name: es-ru
  data_files:
  - split: train
    path: es-ru/train-*
- config_name: es-zh
  data_files:
  - split: train
    path: es-zh/train-*
- config_name: fr-ru
  data_files:
  - split: train
    path: fr-ru/train-*
- config_name: fr-zh
  data_files:
  - split: train
    path: fr-zh/train-*
- config_name: ru-zh
  data_files:
  - split: train
    path: ru-zh/train-*
---

# Dataset Card for United Nations Parallel Corpus

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [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)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://opus.nlpl.eu/UNPC/corpus/version/UNPC
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://aclanthology.org/L16-1561/
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Dataset Summary

The United Nations Parallel Corpus is the first parallel corpus composed from United Nations documents published by the original data creator. 
The parallel corpus consists of manually translated UN documents from the last 25 years (1990 to 2014)
for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
The corpus is freely available for download under a liberal license. 

### Supported Tasks and Leaderboards

The underlying task is machine translation.

### Languages

The six official UN languages: Arabic, Chinese, English, French, Russian, and Spanish.

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

https://conferences.unite.un.org/UNCORPUS/#disclaimer

The following disclaimer, an integral part of the United Nations Parallel Corpus, shall be respected with regard to the Corpus (no other restrictions apply):
- The United Nations Parallel Corpus is made available without warranty of any kind, explicit or implied. The United Nations specifically makes no warranties or representations as to the accuracy or completeness of the information contained in the United Nations Corpus.
- Under no circumstances shall the United Nations be liable for any loss, liability, injury or damage incurred or suffered that is claimed to have resulted from the use of the United Nations Corpus. The use of the United Nations Corpus is at the user's sole risk. The user specifically acknowledges and agrees that the United Nations is not liable for the conduct of any user. If the user is dissatisfied with any of the material provided in the United Nations Corpus, the user's sole and exclusive remedy is to discontinue using the United Nations Corpus.
- When using the United Nations Corpus, the user must acknowledge the United Nations as the source of the information. For references, please cite this reference: Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel Corpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016.
- Nothing herein shall constitute or be considered to be a limitation upon or waiver, express or implied, of the privileges and immunities of the United Nations, which are specifically reserved.

### Citation Information

```
@inproceedings{ziemski-etal-2016-united,
    title = "The {U}nited {N}ations Parallel Corpus v1.0",
    author = "Ziemski, Micha{\\l}  and
      Junczys-Dowmunt, Marcin  and
      Pouliquen, Bruno",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1561",
    pages = "3530--3534",
    abstract = "This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus presented consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal license. Apart from the pairwise aligned documents, a fully aligned subcorpus for the six official UN languages is distributed. We provide baseline BLEU scores of our Moses-based SMT systems trained with the full data of language pairs involving English and for all possible translation directions of the six-way subcorpus.",
}
```
### Contributions

Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.