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Newly-introduced deep learning architectures, namely BERT, XLNet, RoBERTa andALBERT, have been proved to be robust on several NLP tasks. However, thedatasets trained on these architectures are fixed in terms of size andgeneralizability. To relieve this issue, we apply one of the most inexpensivesolutions to update these datasets. We call this approach BET by which weanalyze the backtranslation data augmentation on the transformer-basedarchitectures. Using the Google Translate API with ten intermediary languagesfrom ten different language families, we externally evaluate the results in thecontext of automatic paraphrase identification in a transformer-basedframework. Our findings suggest that BET improves the paraphrase identificationperformance on the Microsoft Research Paraphrase Corpus (MRPC) to more than 3%on both accuracy and F1 score. We also analyze the augmentation in the low-dataregime with downsampled versions of MRPC, Twitter Paraphrase Corpus (TPC) andQuora Question Pairs. In many low-data cases, we observe a switch from afailing model on the test set to reasonable performances. The resultsdemonstrate that BET is a highly promising data augmentation technique: to pushthe current state-of-the-art of existing datasets and to bootstrap theutilization of deep learning architectures in the low-data regime of a hundredsamples.

BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context