ted_multi_translate

  • विवरण :

बड़े पैमाने पर बहुभाषी (60 भाषा) डेटा सेट TED टॉक ट्रांस्क्रिप्ट से प्राप्त किया गया। प्रत्येक रिकॉर्ड में भाषा और पाठ की समानांतर सरणियाँ होती हैं। गुम और अधूरे अनुवादों को फ़िल्टर कर दिया जाएगा।

विभाजित करना उदाहरण
'test' 7,213
'train' 258,098
'validation' 6,049
  • फ़ीचर संरचना :
FeaturesDict({
    'talk_name': Text(shape=(), dtype=string),
    'translations': TranslationVariableLanguages({
        'language': Text(shape=(), dtype=string),
        'translation': Text(shape=(), dtype=string),
    }),
})
  • फ़ीचर दस्तावेज़ीकरण :
विशेषता कक्षा आकार डीटाइप विवरण
विशेषताएं डिक्ट
Talk_name मूलपाठ डोरी
अनुवाद अनुवाद परिवर्तनीय भाषाएं
अनुवाद / भाषा मूलपाठ डोरी
अनुवाद / अनुवाद मूलपाठ डोरी
  • उद्धरण :
@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}
}
,

  • विवरण :

बड़े पैमाने पर बहुभाषी (60 भाषा) डेटा सेट TED टॉक ट्रांस्क्रिप्ट से प्राप्त किया गया। प्रत्येक रिकॉर्ड में भाषा और पाठ की समानांतर सरणियाँ होती हैं। गुम और अधूरे अनुवादों को फ़िल्टर कर दिया जाएगा।

विभाजित करना उदाहरण
'test' 7,213
'train' 258,098
'validation' 6,049
  • फ़ीचर संरचना :
FeaturesDict({
    'talk_name': Text(shape=(), dtype=string),
    'translations': TranslationVariableLanguages({
        'language': Text(shape=(), dtype=string),
        'translation': Text(shape=(), dtype=string),
    }),
})
  • फ़ीचर दस्तावेज़ीकरण :
विशेषता कक्षा आकार डीटाइप विवरण
विशेषताएं डिक्ट
Talk_name मूलपाठ डोरी
अनुवाद अनुवाद परिवर्तनीय भाषाएं
अनुवाद / भाषा मूलपाठ डोरी
अनुवाद / अनुवाद मूलपाठ डोरी
  • उद्धरण :
@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}
}