Augmentation methods for spelling corruptions
SberDevices, Martynov Martynov, Mark Baushenko, Alexander Abramov, Alena Fenogenova · Computational Linguistics and Intellectual Technologies · 2023
The problem of automatic spelling correction is vital to applications such as search engines, chatbots, spellchecking in browsers and text editors. The investigation of spell-checking problems can be divided into several parts: error detection, emulation of the error distribution on the new data for model training, and automatic spelling correction. As the data augmentation technique, the adversarial training via error distribution emulation increases a model’s generalization capabilities; it can address many other challenges: from overcoming a limited amount of training data to regularizing the training objectives of the models. In this work, we propose a novel multi-domain dataset for spelling correction. On this basis, we provide a comparative study of augmentation methods that can be used to emulate the automatic error distribution. We also compare the distribution of the single-domain dataset with the errors from the multi-domain and present a tool that can emulate human misspellings.