# DPRK-BERT: The Supreme Language Model

arXiv: https://arxiv.org/abs/2112.00567 · HTML: https://mlsanity.com/paper/2112.00567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [Multilingual Alignment of Contextual Word Representations](https://mlsanity.com/paper/2002.03518/) (2020)
- [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019)
- [KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding](https://mlsanity.com/paper/2004.03289/) (2020)
- [Neural Domain Adaptation for Biomedical Question Answering](https://mlsanity.com/paper/1706.03610/) (2017)
- [KR-BERT: A Small-Scale Korean-Specific Language Model](https://mlsanity.com/paper/2008.03979/) (2020)

## Abstract (arXiv)

Deep language models have achieved remarkable success in the NLP domain. The standard way to train a deep language model is to employ unsupervised learning from scratch on a large unlabeled corpus. However, such large corpora are only available for widely-adopted and high-resource languages and domains. This study presents the first deep language model, DPRK-BERT, for the DPRK language. We achieve this by compiling the first unlabeled corpus for the DPRK language and fine-tuning a preexisting the ROK language model. We compare the proposed model with existing approaches and show significant improvements on two DPRK datasets. We also present a cross-lingual version of this model which yields better generalization across the two Korean languages. Finally, we provide various NLP tools related to the DPRK language that would foster future research.