Google I/O — это обертка! Наверстать упущенное в сеансах TensorFlow Просмотреть сеансы


  • Описание :

Массивный многоязычный (60 языков) набор данных, полученный из расшифровок TED Talk. Каждая запись состоит из параллельных массивов языка и текста. Отсутствующие и неполные переводы будут отфильтрованы.

Расколоть Примеры
'test' 7 213
'train' 258 098
'validation' 6049
  • Структура функции :
    'talk_name': Text(shape=(), dtype=tf.string),
    'translations': TranslationVariableLanguages({
        'language': Text(shape=(), dtype=tf.string),
        'translation': Text(shape=(), dtype=tf.string),
  • Документация по функциям :
Характерная черта Сорт Форма Dтип Описание
talk_name Текст tf.string
переводы ПереводПеременнаяЯзыки
переводы/язык Текст tf.string
переводы/перевод Текст tf.string
  • Цитата :
  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       = {}