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

• URL: http://arxiv.org/abs/2009.12452v1
• Date: Fri, 25 Sep 2020 22:06:06 GMT
• Title: BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context
• Authors: Jean-Philippe Corbeil and Hadi Abdi Ghadivel
• Abstract summary: We call this approach BET by which we analyze the backtranslation data augmentation on the transformer-based architectures. Our findings suggest that BET improves the paraphrase identification performance on the Microsoft Research Paraphrase Corpus to more than 3% on both accuracy and F1 score.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Newly-introduced deep learning architectures, namely BERT, XLNet, RoBERTa and ALBERT, have been proved to be robust on several NLP tasks. However, the datasets trained on these architectures are fixed in terms of size and generalizability. To relieve this issue, we apply one of the most inexpensive solutions to update these datasets. We call this approach BET by which we analyze the backtranslation data augmentation on the transformer-based architectures. Using the Google Translate API with ten intermediary languages from ten different language families, we externally evaluate the results in the context of automatic paraphrase identification in a transformer-based framework. Our findings suggest that BET improves the paraphrase identification performance 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-data regime with downsampled versions of MRPC, Twitter Paraphrase Corpus (TPC) and Quora Question Pairs. In many low-data cases, we observe a switch from a failing model on the test set to reasonable performances. The results demonstrate that BET is a highly promising data augmentation technique: to push the current state-of-the-art of existing datasets and to bootstrap the utilization of deep learning architectures in the low-data regime of a hundred samples.

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