# Learning How to Translate North Korean through South Korean

arXiv: https://arxiv.org/abs/2201.11258 · HTML: https://mlsanity.com/paper/2201.11258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings](https://mlsanity.com/paper/1811.01136/) (2018)
- [Learning Joint Multilingual Sentence Representations with Neural Machine Translation](https://mlsanity.com/paper/1704.04154/) (2017)
- [Domain Adaptation of Neural Machine Translation by Lexicon Induction](https://mlsanity.com/paper/1906.00376/) (2019)
- [Filtering and Mining Parallel Data in a Joint Multilingual Space](https://mlsanity.com/paper/1805.09822/) (2018)
- [Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings](https://mlsanity.com/paper/2010.07761/) (2020)
- [Neural Machine Translation into Language Varieties](https://mlsanity.com/paper/1811.01064/) (2018)
- [Extracting an English-Persian Parallel Corpus from Comparable Corpora](https://mlsanity.com/paper/1711.00681/) (2017)

## Abstract (arXiv)

South and North Korea both use the Korean language. However, Korean NLP research has focused on South Korean only, and existing NLP systems of the Korean language, such as neural machine translation (NMT) models, cannot properly handle North Korean inputs. Training a model using North Korean data is the most straightforward approach to solving this problem, but there is insufficient data to train NMT models. In this study, we create data for North Korean NMT models using a comparable corpus. First, we manually create evaluation data for automatic alignment and machine translation. Then, we investigate automatic alignment methods suitable for North Korean. Finally, we verify that a model trained by North Korean bilingual data without human annotation can significantly boost North Korean translation accuracy compared to existing South Korean models in zero-shot settings.