  Universal Language Model for Fine-tuning [arXiv:1801.06146] (ULMFiT) is one
of the first NLP methods for efficient inductive transfer learning.
Unsupervised pretraining results in improvements on many NLP tasks for English.
In this paper, we describe a new method that uses subword tokenization to adapt
ULMFiT to languages with high inflection. Our approach results in a new
state-of-the-art for the Polish language, taking first place in Task 3 of
PolEval'18. After further training, our final model outperformed the second
best model by 35%. We have open-sourced our pretrained models and code.
