Augmenty: A Python Library for Structured Text Augmentation

Text augmentation is useful for tool for training (Wei and Zou 2019) and evaluating (Ribeiro et al. 2020) natural language processing models and systems. Despite its utility existing libraries for text augmentation often exhibit limitations in terms of functionality and flexibility, being confined to basic tasks such as text-classification or cater to specific downstream use-cases such as estimating robustness (Goel et al. 2021). Recognizing these constraints, Augmenty is a tool for structured text augmentation of the text along with its annotations. Augmenty integrates seamlessly with the popular NLP library spaCy (Honnibal et al. 2020) and seeks to be compatible with all models and tasks supported by spaCy. Augmenty provides a wide range of augmenters which can be combined in a flexible manner to create complex augmentation pipelines. It also includes a set of primitives that can be used to create custom augmenters such as word replacement augmenters. This functionality allows for augmentations within a range of applications such as named entity recognition (NER), part-of-speech tagging, and dependency parsing.