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

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 335.91 MB
  • Size of the generated dataset: 754.37 MB
  • Total amount of disk used: 1090.27 MB

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.


  • translations: a multilingual string variable, with possible languages including ar, az, be, bg, bn.
  • talk_name: a string feature.

Data Splits Sample Size

name train validation test
plain_text 258098 6049 7213

Dataset Creation

Curation Rationale

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Source Data

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

  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       = {}


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.

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