uaritm/multilingual_en_ru_uk
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Dataset Card for "ted_multi"
Dataset Summary
Massively multilingual (60 language) data set derived from TED Talk transcripts. Each record consists of parallel arrays of language and text. Missing and incomplete translations will be filtered out.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
plain_text
- Size of downloaded dataset files: 352.23 MB
- Size of the generated dataset: 791.01 MB
- Total amount of disk used: 1.14 GB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"talk_name": "shabana_basij_rasikh_dare_to_educate_afghan_girls",
"translations": "{\"language\": [\"ar\", \"az\", \"bg\", \"bn\", \"cs\", \"da\", \"de\", \"el\", \"en\", \"es\", \"fa\", \"fr\", \"he\", \"hi\", \"hr\", \"hu\", \"hy\", \"id\", \"it\", ..."
}
Data Fields
The data fields are the same among all splits.
plain_text
translations: a multilingualstringvariable, with possible languages includingar,az,be,bg,bn.talk_name: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| plain_text | 258098 | 6049 | 7213 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{qi-EtAl:2018:N18-2,
author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},
title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
month = {June},
year = {2018},
address = {New Orleans, Louisiana},
publisher = {Association for Computational Linguistics},
pages = {529--535},
abstract = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
url = {http://www.aclweb.org/anthology/N18-2084}
}
Contributions
Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
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