Implementing back translation as a data augmentation for text classification2019 Community Moderator ElectionAre there libraries or techniques for 'noisifying' text data?Data Augmentation in videosData Augmentation in TensorflowData Augmentation for RegressionIs image data augmentation breaking the distribution?Data augmentation: rotating images and zero valuesGAN's for data augmentationData Augmentation recommended pipelineData Augmentation for Regression ANN with low Sample SizeOn a multi lingual sentiment corpus
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Implementing back translation as a data augmentation for text classification
2019 Community Moderator ElectionAre there libraries or techniques for 'noisifying' text data?Data Augmentation in videosData Augmentation in TensorflowData Augmentation for RegressionIs image data augmentation breaking the distribution?Data augmentation: rotating images and zero valuesGAN's for data augmentationData Augmentation recommended pipelineData Augmentation for Regression ANN with low Sample SizeOn a multi lingual sentiment corpus
$begingroup$
Since back translation English->other language -> English seems like quite a useful data augmentation technique , I wanted to experiment with it. E.g. it occurred to me that languages from very different language families (but very well supported for economic reasons such as Chinese, Russian, Spanish, Korean, Arabic...) could make for a diverse set of effects occurring in the back translation.
Commercial translation APIs would be a straightforward way of doing this, but without free API key or budget from my organization (would not qualify as academic) that's quickly quite expensive for a private thing.
Pretrained translation models would seem like an obvious alternative (I have a GPU for inference, but clearly that's not enough to train all the models from scratch), but I could e.g. not find those for any OpenNMT variant. Are there any recommendations from others that have used this approach?
deep-learning nlp text data-augmentation machine-translation
asked Mar 29 at 7:25
BjörnBjörn
243111
$endgroup$
add a comment |
$begingroup$
Since back translation English->other language -> English seems like quite a useful data augmentation technique , I wanted to experiment with it. E.g. it occurred to me that languages from very different language families (but very well supported for economic reasons such as Chinese, Russian, Spanish, Korean, Arabic...) could make for a diverse set of effects occurring in the back translation.
Commercial translation APIs would be a straightforward way of doing this, but without free API key or budget from my organization (would not qualify as academic) that's quickly quite expensive for a private thing.
Pretrained translation models would seem like an obvious alternative (I have a GPU for inference, but clearly that's not enough to train all the models from scratch), but I could e.g. not find those for any OpenNMT variant. Are there any recommendations from others that have used this approach?
deep-learning nlp text data-augmentation machine-translation
asked Mar 29 at 7:25
BjörnBjörn
243111
$endgroup$
add a comment |
$begingroup$
Since back translation English->other language -> English seems like quite a useful data augmentation technique , I wanted to experiment with it. E.g. it occurred to me that languages from very different language families (but very well supported for economic reasons such as Chinese, Russian, Spanish, Korean, Arabic...) could make for a diverse set of effects occurring in the back translation.
Commercial translation APIs would be a straightforward way of doing this, but without free API key or budget from my organization (would not qualify as academic) that's quickly quite expensive for a private thing.
Pretrained translation models would seem like an obvious alternative (I have a GPU for inference, but clearly that's not enough to train all the models from scratch), but I could e.g. not find those for any OpenNMT variant. Are there any recommendations from others that have used this approach?
deep-learning nlp text data-augmentation machine-translation
asked Mar 29 at 7:25
BjörnBjörn
243111
$endgroup$
Since back translation English->other language -> English seems like quite a useful data augmentation technique , I wanted to experiment with it. E.g. it occurred to me that languages from very different language families (but very well supported for economic reasons such as Chinese, Russian, Spanish, Korean, Arabic...) could make for a diverse set of effects occurring in the back translation.
Commercial translation APIs would be a straightforward way of doing this, but without free API key or budget from my organization (would not qualify as academic) that's quickly quite expensive for a private thing.
Pretrained translation models would seem like an obvious alternative (I have a GPU for inference, but clearly that's not enough to train all the models from scratch), but I could e.g. not find those for any OpenNMT variant. Are there any recommendations from others that have used this approach?
deep-learning nlp text data-augmentation machine-translation
deep-learning nlp text data-augmentation machine-translation
asked Mar 29 at 7:25
BjörnBjörn
243111
asked Mar 29 at 7:25
BjörnBjörn
243111
asked Mar 29 at 7:25
BjörnBjörn
243111
asked Mar 29 at 7:25
BjörnBjörn
243111
asked Mar 29 at 7:25
BjörnBjörn
243111
243111
add a comment |
add a comment |
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