  A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional
representation learning for natural languages. Due to the lack of pretrained
ALBERT models for Korean language, the best available practice is the
multilingual model or resorting back to the any other BERT-based model. In this
paper, we develop and pretrain KoreALBERT, a monolingual ALBERT model
specifically for Korean language understanding. We introduce a new training
objective, namely Word Order Prediction (WOP), and use alongside the existing
MLM and SOP criteria to the same architecture and model parameters. Despite
having significantly fewer model parameters (thus, quicker to train), our
pretrained KoreALBERT outperforms its BERT counterpart on 6 different NLU
tasks. Consistent with the empirical results in English by Lan et al.,
KoreALBERT seems to improve downstream task performance involving
multi-sentence encoding for Korean language. The pretrained KoreALBERT is
publicly available to encourage research and application development for Korean
NLP.
