[Needs More Information]

# Dataset Card for allenai/wmt22_african

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [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)

## Dataset Description

- **Homepage:** https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

This dataset was created based on [metadata](https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african) for mined bitext released by Meta AI.  It contains bitext for 248 pairs for the African languages that are part of the [2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages](https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html).

#### How to use the data

There are two ways to access the data:

* Via the Hugging Face Python datasets library 

```
from datasets import load_dataset
dataset = load_dataset("allenai/wmt22_african")
```

* Clone the git repo
```
git lfs install
git clone https://huggingface.co/datasets/allenai/wmt22_african
```


### Supported Tasks and Leaderboards

This dataset is one of resources allowed under the Constrained Track for the [2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages](https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html).

### Languages

#### Focus languages

| Language | Code |
| -------- | ---- |
| Afrikaans | afr |
| Amharic | amh |
| Chichewa | nya |
| Nigerian Fulfulde | fuv |
| Hausa | hau |
| Igbo | ibo |
| Kamba | kam |
| Kinyarwanda | kin |
| Lingala | lin |
| Luganda | lug |
| Luo | luo |
| Northern Sotho | nso |
| Oroma | orm |
| Shona | sna |
| Somali | som |
| Swahili | swh |
| Swati | ssw |
| Tswana | tsn | 
| Umbundu | umb |
| Wolof | wol |
| Xhosa | xho |
| Xitsonga | tso |
| Yoruba | yor |
| Zulu | zul |

Colonial linguae francae: English - eng, French - fra

## Dataset Structure

The dataset contains gzipped tab delimited text files for each direction.  Each text file contains lines with parallel sentences.

### Data Instances

The dataset contains 248 language pairs.

Columns are:
source_sentence target_sentence laser_score source_sentence_lid target_sentence_lid where lid is language classification probability

Here are the sentence counts for each pair:\
1621007	afr-eng \
1172757	afr-som \
497739	amh-eng\
1888196	amh-fra\
566422	amh-nya\
89763	amh-orm\
844829	amh-sna\
491233	amh-som\
52337	amh-ssw\
1013477	amh-swh\
257342	amh-tsn\
231190	amh-tso\
99902	amh-umb\
508311	amh-xho\
399634	amh-yor\
834986	amh-zul\
1372999	eng-fuv\
2309758	eng-hau\
172973	eng-ibo\
1656141	eng-kam\
9732858	eng-kin\
2890688	eng-lin\
3450573	eng-lug\
2767100	eng-luo\
3043677	eng-nso\
1548650	eng-nya\
2793755	eng-orm\
8782707	eng-sna\
576601	eng-som\
165712	eng-ssw\
23358739	eng-swh\
5931529	eng-tsn\
630860	eng-tso\
302901	eng-umb\
95678	eng-wol\
8690985	eng-xho\
1455571	eng-yor\
3862020	eng-zul\
372003	fra-hau\
630593	fra-ibo\
198309	fra-kam\
1289491	fra-kin\
347026	fra-lin\
377017	fra-lug\
295465	fra-luo\
321118	fra-nso\
1170250	fra-nya\
319649	fra-orm\
1256559	fra-som\
119523	fra-ssw\
2607867	fra-swh\
630801	fra-tsn\
440861	fra-tso\
236624	fra-umb\
189659	fra-wol\
1092123	fra-xho\
1760905	fra-zul\
227958	fuv-hau\
89652	fuv-ibo\
13571	fuv-kam\
192596	fuv-kin\
79341	fuv-lug\
50756	fuv-luo\
42429	fuv-nso\
189176	fuv-nya
67398	fuv-orm\
106809	fuv-sna\
203640	fuv-som\
19283	fuv-ssw\
275428	fuv-swh\
74068	fuv-tsn\
55015	fuv-tso\
27888	fuv-umb\
138286	fuv-xho\
331301	fuv-yor\
150846	fuv-zul\
247694	hau-ibo\
90033	hau-kam\
317291	hau-kin\
169056	hau-lug\
152246	hau-luo\
158432	hau-nso\
1141968	hau-nya\
101928	hau-orm\
780160	hau-sna\
490683	hau-som\
73076	hau-ssw\
893732	hau-swh\
265892	hau-tsn\
213552	hau-tso\
111124	hau-umb\
596312	hau-xho\
762819	hau-yor\
796053	hau-zul\
33966	ibo-kam\
154467	ibo-kin\
91272	ibo-lug\
71387	ibo-luo\
81767	ibo-nso\
486357	ibo-nya\
52249	ibo-orm\
444070	ibo-sna\
337727	ibo-som\
36426	ibo-ssw\
479101	ibo-swh\
131142	ibo-tsn\
99214	ibo-tso\
48163	ibo-umb\
323382	ibo-xho\
378378	ibo-yor\
491925	ibo-zul\
74809	kam-kin\
52158	kam-lug\
39193	kam-luo\
35061	kam-nso\
92704	kam-nya\
33964	kam-orm\
94385	kam-sna\
84297	kam-som\
16222	kam-ssw\
223474	kam-swh\
69242	kam-tsn\
73198	kam-tso\
41157	kam-umb\
80998	kam-xho\
69432	kam-yor\
114922	kam-zul\
188222	kin-lug\
157234	kin-luo\
196675	kin-nso\
389725	kin-nya\
101820	kin-orm\
385576	kin-sna\
258130	kin-som\
85684	kin-ssw\
743661	kin-swh\
268221	kin-tsn\
315691	kin-tso\
122759	kin-umb\
361464	kin-xho\
213902	kin-yor\
492158	kin-zul\
105776	lug-luo\
107569	lug-nso\
183247	lug-nya\
64732	lug-orm\
197359	lug-sna\
131828	lug-som\
51518	lug-ssw\
325122	lug-swh\
175387	lug-tsn\
148662	lug-tso\
75469	lug-umb\
154149	lug-xho\
137179	lug-yor\
194564	lug-zul\
87376	luo-nso\
166259	luo-nya\
48212	luo-orm\
204663	luo-sna\
123244	luo-som\
38356	luo-ssw\
324826	luo-swh\
133960	luo-tsn\
132306	luo-tso\
68896	luo-umb\
143748	luo-xho\
110753	luo-yor\
196325	luo-zul\
154111	nso-nya\
70340	nso-orm\
155175	nso-sna\
130594	nso-som\
74696	nso-ssw\
307206	nso-swh\
234768	nso-tsn\
212052	nso-tso\
63006	nso-umb\
200563	nso-xho\
148906	nso-yor\
230661	nso-zul\
82514	nya-orm\
976015	nya-sna\
516451	nya-som\
76598	nya-ssw\
1078568	nya-swh\
276998	nya-tsn\
350167	nya-tso\
141972	nya-umb\
698857	nya-xho\
512418	nya-yor\
1062461	nya-zul\
91493	orm-sna\
83049	orm-som\
31701	orm-ssw\
178212	orm-swh\
97553	orm-tsn\
78559	orm-tso\
44331	orm-umb\
95505	orm-xho\
73868	orm-yor\
92733	orm-zul\
511185	sna-som\
76168	sna-ssw\
1095473	sna-swh\
287574	sna-tsn\
336898	sna-tso\
152770	sna-umb\
842612	sna-xho\
524739	sna-yor\
1160370	sna-zul\
61247	som-ssw\
604372	som-swh\
179485	som-tsn\
177327	som-tso\
93461	som-umb\
69318	som-wol\
362513	som-xho\
355099	som-yor\
506404	som-zul\
147869	ssw-swh\
85369	ssw-tsn\
101540	ssw-tso\
29533	ssw-umb\
97437	ssw-xho\
66000	ssw-yor\
142991	ssw-zul\
480942	swh-tsn\
553410	swh-tso\
276967	swh-umb\
785796	swh-xho\
559321	swh-yor\
1240423	swh-zul\
285124	tsn-tso\
107024	tsn-umb\
287133	tsn-xho\
194308	tsn-yor\
341119	tsn-zul\
128803	tso-umb\
383556	tso-xho\
168359	tso-yor\
471398	tso-zul\
132264	umb-xho\
81309	umb-yor\
181634	umb-zul\
371261	xho-yor\
1066327	xho-zul\
560858	yor-zul

### Data Fields

Every instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser_score', 'source_sentence_lid', 'target_sentence_lid'.

Example:
```
{
'translation': 
    {
        'afr': 'In Mei 2007, in ooreenstemming met die spesifikasies van die Java Gemeenskapproses, het Sun Java tegnologie geherlisensieer onder die GNU General Public License.', 
        'eng': 'As of May 2007, in compliance with the specifications of the Java Community Process, Sun relicensed most of its Java technologies under the GNU General Public License.'
    }, 
'laser_score': 1.0717015266418457, 
'source_sentence_lid': 0.9996600151062012, 
'target_sentence_lid': 0.9972000122070312
}
```
### Data Splits

The data is not split into train, dev, and test.

## Dataset Creation

### Curation Rationale

Parallel sentences from monolingual data in Common Crawl and ParaCrawl were identified via [Language-Agnostic Sentence Representation (LASER)](https://github.com/facebookresearch/LASER) encoders.

### Source Data

#### Initial Data Collection and Normalization

Monolingual data was obtained from Common Crawl and ParaCrawl.

#### Who are the source language producers?

Contributors to web text in Common Crawl and ParaCrawl.

### Annotations

#### Annotation process

The data was not human annotated. The metadata used to create the dataset can be found here: https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african

#### Who are the annotators?

The data was not human annotated.  Parallel text from Common Crawl and Para Crawl monolingual data were identified automatically via [LASER](https://github.com/facebookresearch/LASER) encoders. 

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

This dataset provides data for training machine learning systems for many languages that have low resources available for NLP.

### Discussion of Biases

Biases in the data have not been studied.

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

We are releasing this dataset under the terms of [CC-BY-NC](https://github.com/facebookresearch/LASER/blob/main/data/wmt22_african/LICENSE).

### Citation Information

Forthcoming research paper that describes the approach used to create the metadata.  Citation Information will be updated with the paper information when that is available.