  Natural Language Processing (NLP) relies heavily on training data.
Transformers, as they have gotten bigger, have required massive amounts of
training data. To satisfy this requirement, text augmentation should be looked
at as a way to expand your current dataset and to generalize your models. One
text augmentation we will look at is translation augmentation. We take an
English sentence and translate it to another language before translating it
back to English. In this paper, we look at the effect of 108 different language
back translations on various metrics and text embeddings.
