- توضیحات :
مجموعه داده انبوه چند زبانه (60 زبان) برگرفته از رونوشتهای گفتگوی TED. هر رکورد از آرایه های موازی زبان و متن تشکیل شده است. ترجمه های مفقود و ناقص فیلتر خواهند شد.
صفحه اصلی : https://github.com/neulab/word-embeddings-for-nmt
نسخه ها :
-
1.1.0
(پیش فرض): بدون یادداشت انتشار.
-
حجم دانلود :
335.91 MiB
حجم مجموعه داده :
752.30 MiB
ذخیره خودکار ( اسناد ): خیر
تقسیم ها :
شکاف | مثال ها |
---|---|
'test' | 7213 |
'train' | 258,098 |
'validation' | 6,049 |
- ساختار ویژگی :
FeaturesDict({
'talk_name': Text(shape=(), dtype=string),
'translations': TranslationVariableLanguages({
'language': Text(shape=(), dtype=string),
'translation': Text(shape=(), dtype=string),
}),
})
- مستندات ویژگی :
ویژگی | کلاس | شکل | نوع D | شرح |
---|---|---|---|---|
FeaturesDict | ||||
talk_name | متن | رشته | ||
ترجمه ها | TranslationVariableLanguages | |||
ترجمه ها/زبان | متن | رشته | ||
ترجمه/ترجمه | متن | رشته |
کلیدهای نظارت شده (به
as_supervised
doc مراجعه کنید):None
شکل ( tfds.show_examples ): پشتیبانی نمی شود.
مثالها ( tfds.as_dataframe ):
- نقل قول :
@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}
}
، - توضیحات :
مجموعه داده انبوه چند زبانه (60 زبان) برگرفته از رونوشتهای گفتگوی TED. هر رکورد از آرایه های موازی زبان و متن تشکیل شده است. ترجمه های مفقود و ناقص فیلتر خواهند شد.
صفحه اصلی : https://github.com/neulab/word-embeddings-for-nmt
نسخه ها :
-
1.1.0
(پیش فرض): بدون یادداشت انتشار.
-
حجم دانلود :
335.91 MiB
حجم مجموعه داده :
752.30 MiB
ذخیره خودکار ( اسناد ): خیر
تقسیم ها :
شکاف | مثال ها |
---|---|
'test' | 7213 |
'train' | 258,098 |
'validation' | 6,049 |
- ساختار ویژگی :
FeaturesDict({
'talk_name': Text(shape=(), dtype=string),
'translations': TranslationVariableLanguages({
'language': Text(shape=(), dtype=string),
'translation': Text(shape=(), dtype=string),
}),
})
- مستندات ویژگی :
ویژگی | کلاس | شکل | نوع D | شرح |
---|---|---|---|---|
FeaturesDict | ||||
talk_name | متن | رشته | ||
ترجمه ها | TranslationVariableLanguages | |||
ترجمه ها/زبان | متن | رشته | ||
ترجمه/ترجمه | متن | رشته |
کلیدهای نظارت شده (به
as_supervised
doc مراجعه کنید):None
شکل ( tfds.show_examples ): پشتیبانی نمی شود.
مثالها ( tfds.as_dataframe ):
- نقل قول :
@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}
}