# Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue

arXiv: https://arxiv.org/abs/1911.11008 · HTML: https://mlsanity.com/paper/1911.11008/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training](https://mlsanity.com/paper/2507.10920/) (2025)
- [Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation](https://mlsanity.com/paper/2605.07533/) (2026)
- [TransClean: A Benchmark for Detecting and Extracting Clean Translations from Large Language Model Outputs](https://mlsanity.com/paper/2609.11399/) (2026)

## Abstract (arXiv)

Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary. Sino-Korean words, which can be converted into corresponding Chinese characters, account for more than fifty of the entire Korean vocabulary. Motivated by this, we propose a simple linguistically motivated solution to improve the performance of the Korean-to-Chinese neural machine translation model by using their common vocabulary. We adopt Chinese characters as a translation pivot by converting Sino-Korean words in Korean sentences to Chinese characters and then train the machine translation model with the converted Korean sentences as source sentences. The experimental results on Korean-to-Chinese translation demonstrate that the models with the proposed method improve translation quality up to 1.5 BLEU points in comparison to the baseline models.