ted_multi_translate

• Descriptif :

Ensemble de données massivement multilingue (60 langues) dérivé des transcriptions de TED Talk. Chaque enregistrement se compose de tableaux parallèles de langue et de texte. Les traductions manquantes et incomplètes seront filtrées.

• Page d'accueil : https://github.com/neulab/word-embeddings-for-nmt

• Code source : tfds.datasets.ted_multi_translate.Builder

• Versions :

  • 1.1.0 (par défaut) : aucune note de version.

• Taille du téléchargement : 335.91 MiB

• Taille du jeu de données : 752.30 MiB

• Mise en cache automatique ( documentation ): Non

• Fractionnements :

• Structure des fonctionnalités :

FeaturesDict({
    'talk_name': Text(shape=(), dtype=string),
    'translations': TranslationVariableLanguages({
        'language': Text(shape=(), dtype=string),
        'translation': Text(shape=(), dtype=string),
    }),
})

• Documentation des fonctionnalités :

• Clés supervisées (Voir as_supervised doc ): None

• Figure ( tfds.show_examples ) : non pris en charge.

• Exemples ( tfds.as_dataframe ):

• Citation :

@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}
}

• Descriptif :

Ensemble de données massivement multilingue (60 langues) dérivé des transcriptions de TED Talk. Chaque enregistrement se compose de tableaux parallèles de langue et de texte. Les traductions manquantes et incomplètes seront filtrées.

• Page d'accueil : https://github.com/neulab/word-embeddings-for-nmt

• Code source : tfds.datasets.ted_multi_translate.Builder

• Versions :

  • 1.1.0 (par défaut) : aucune note de version.

• Taille du téléchargement : 335.91 MiB

• Taille du jeu de données : 752.30 MiB

• Mise en cache automatique ( documentation ): Non

• Fractionnements :

• Structure des fonctionnalités :

FeaturesDict({
    'talk_name': Text(shape=(), dtype=string),
    'translations': TranslationVariableLanguages({
        'language': Text(shape=(), dtype=string),
        'translation': Text(shape=(), dtype=string),
    }),
})

• Documentation des fonctionnalités :

• Clés supervisées (Voir as_supervised doc ): None

• Figure ( tfds.show_examples ) : non pris en charge.

• Exemples ( tfds.as_dataframe ):

• Citation :

@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}
}