# Character-level Chinese-English Translation through ASCII Encoding

arXiv: https://arxiv.org/abs/1805.03330 · HTML: https://mlsanity.com/paper/1805.03330/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 4 of 4)

- [Robust Open-Vocabulary Translation from Visual Text Representations](https://mlsanity.com/paper/2104.08211/) (2021)
- [Why don't people use character-level machine translation?](https://mlsanity.com/paper/2110.08191/) (2021)
- [M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation](https://mlsanity.com/paper/2406.08255/) (2024)
- [CNM-BERT: A Drop-In Structural Embedding for Chinese Characters via Ideographic Description Sequences](https://mlsanity.com/paper/2608.05167/) (2026)

## Abstract (arXiv)

Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme, which preserves the original shape and semantic information of the characters, while also being reversible. We show promising results from training Wubi-based models on the character- and subword-level with recurrent as well as convolutional models.