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

TL;DR

This paper introduces a novel approach for Korean-to-Chinese translation by leveraging Chinese characters as a pivot, converting Sino-Korean words to improve translation accuracy in a low-resource setting.

Contribution

The paper proposes using Chinese characters as a pivot to enhance Korean-to-Chinese neural machine translation by exploiting shared vocabulary, demonstrating improved BLEU scores.

Findings

Up to 1.5 BLEU point improvement over baseline

Effective use of Sino-Korean words as shared vocabulary

Simple linguistically motivated method

Abstract

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.