Before revision: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.). Motivated by a business application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics.
After revision: Text data augmentation, i.e. the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. Machine Translation, Text Classification). Initially motivated by an application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics.