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Dataset Summary
This dataset provides the data for mLAMA, a multilingual version of LAMA. Regarding LAMA see https://github.com/facebookresearch/LAMA. For mLAMA the TREx and GoogleRE part of LAMA was considered and machine translated using Google Translate, and the Wikidata and Google Knowledge Graph API. The machine translated templates were checked for validity, i.e., whether they contain exactly one '[X]' and one '[Y]'.
This data can be used for creating fill-in-the-blank queries like "Paris is the capital of [MASK]" across 53 languages. For more details see the website http://cistern.cis.lmu.de/mlama/ or the github repo https://github.com/norakassner/mlama.
Supported Tasks and Leaderboards
Language model knowledge probing.
Languages
This dataset contains data in 53 languages: af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh
Dataset Structure
For each of the 53 languages and each of the 43 relations/predicates there is a set of triples.
Data Instances
For each language and relation there are triples, that consists of an object, a predicate and a subject. For each predicate there is a template available. An example for dataset["test"][0]
is given here:
{
'language': 'af',
'lineid': 0,
'obj_label': 'Frankryk',
'obj_uri': 'Q142',
'predicate_id': 'P1001',
'sub_label': 'President van Frankryk',
'sub_uri': 'Q191954',
'template': "[X] is 'n wettige term in [Y].",
'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'
}
Data Fields
Each instance has the following fields
- "uuid": a unique identifier
- "lineid": a identifier unique to mlama
- "obj_id": knowledge graph id of the object
- "obj_label": surface form of the object
- "sub_id": knowledge graph id of the subject
- "sub_label": surface form of the subject
- "template": template
- "language": language code
- "predicate_id": relation id
Data Splits
There is only one partition that is labelled as 'test data'.
Dataset Creation
Curation Rationale
The dataset was translated into 53 languages to investigate knowledge in pretrained language models multilingually.
Source Data
Initial Data Collection and Normalization
The data has several sources:
LAMA (https://github.com/facebookresearch/LAMA) licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) T-REx (https://hadyelsahar.github.io/t-rex/) licensed under Creative Commons Attribution-ShareAlike 4.0 International License Google-RE (https://github.com/google-research-datasets/relation-extraction-corpus) Wikidata (https://www.wikidata.org/) licensed under Creative Commons CC0 License and Creative Commons Attribution-ShareAlike License
Who are the source language producers?
See links above.
Annotations
Annotation process
Crowdsourced (wikidata) and machine translated.
Who are the annotators?
Unknown.
Personal and Sensitive Information
Names of (most likely) famous people who have entries in Google Knowledge Graph or Wikidata.
Considerations for Using the Data
Data was created through machine translation and automatic processes.
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
Not all triples are available in all languages.
Additional Information
Dataset Curators
The authors of the mLAMA paper and the authors of the original datasets.
Licensing Information
The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/
Citation Information
@article{kassner2021multilingual,
author = {Nora Kassner and
Philipp Dufter and
Hinrich Sch{\"{u}}tze},
title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
Language Models},
journal = {CoRR},
volume = {abs/2102.00894},
year = {2021},
url = {https://arxiv.org/abs/2102.00894},
archivePrefix = {arXiv},
eprint = {2102.00894},
timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
note = {to appear in EACL2021}
}
Contributions
Thanks to @pdufter for adding this dataset.
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