Low Resource Text Classification with ULMFit and Backtranslation
Sam Shleifer Stanford University shleifer [at] stanford.edu
Abstract
In computer vision, virtually every state of the art deep learning system is trained with data augmentation. In text classification, however, data augmentation is less widely practiced because it must be performed before training and risks introducing label noise. We augment the IMDB movie reviews dataset with examples generated by two families of techniques: random token perturbations introduced by Wei and Zou (2019) and backtranslation – translating to a second language then back to English.
原文 arXiv:1903.09244;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1903.09244v2