BERT-based models are currently used for solving nearly all Natural Language
Processing (NLP) tasks and most often achieve state-of-the-art results.
Therefore, the NLP community conducts extensive research on understanding these
models, but above all on designing effective and efficient training procedures.
Several ablation studies investigating how to train BERT-like models have been
carried out, but the vast majority of them concerned only the English language.
A training procedure designed for English does not have to be universal and
applicable to other especially typologically different languages. Therefore,
this paper presents the first ablation study focused on Polish, which, unlike
the isolating English language, is a fusional language. We design and
thoroughly evaluate a pretraining procedure of transferring knowledge from
multilingual to monolingual BERT-based models. In addition to multilingual
model initialization, other factors that possibly influence pretraining are
also explored, i.e. training objective, corpus size, BPE-Dropout, and
pretraining length. Based on the proposed procedure, a Polish BERT-based
language model -- HerBERT -- is trained. This model achieves state-of-the-art
results on multiple downstream tasks.

本文介绍了第一项针对波兰语的去构建 BERT 的训练过程的消融研究，其中探讨了跨语言训练和其他因素，最终实现了一个波兰语 BERT 模型 HerBERT，并在多个下游任务中达到了最佳结果。