# An Empirical Study on Chinese Character Decomposition in Multiword Expression-Aware Neural Machine Translation

arXiv: https://arxiv.org/abs/2512.15556 · HTML: https://mlsanity.com/paper/2512.15556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 78 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016)
- [BLEU might be Guilty but References are not Innocent](https://mlsanity.com/paper/2004.06063/) (2020)
- [Glyce: Glyph-vectors for Chinese Character Representations](https://mlsanity.com/paper/1901.10125/) (2019)
- [A Set of Recommendations for Assessing Human-Machine Parity in Language Translation](https://mlsanity.com/paper/2004.01694/) (2020)
- [Character-based Neural Machine Translation](https://mlsanity.com/paper/1603.00810/) (2016)
- [Optimizing Segmentation Granularity for Neural Machine Translation](https://mlsanity.com/paper/1810.08641/) (2018)
- [Chinese Character Decomposition for Neural MT with Multi-Word Expressions](https://mlsanity.com/paper/2104.04497/) (2021)
- [Neural Name Translation Improves Neural Machine Translation](https://mlsanity.com/paper/1607.01856/) (2016)
- [LEPOR: An Augmented Machine Translation Evaluation Metric](https://mlsanity.com/paper/1703.08748/) (2017)
- [MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora](https://mlsanity.com/paper/2005.10583/) (2020)
- [Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date overview](https://mlsanity.com/paper/1605.04515/) (2016)
- [A Sub-Character Architecture for Korean Language Processing](https://mlsanity.com/paper/1707.06341/) (2017)
- [Paying Attention to Multi-Word Expressions in Neural Machine Translation](https://mlsanity.com/paper/1710.06313/) (2017)
- [Neural Machine Translation of Logographic Languages Using Sub-character Level Information](https://mlsanity.com/paper/1809.02694/) (2018)
- [Improving Character-level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature](https://mlsanity.com/paper/1805.02937/) (2018)
- [Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese](https://mlsanity.com/paper/1708.03312/) (2017)

## Abstract (arXiv)

Word meaning, representation, and interpretation play fundamental roles in natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) tasks. Many of the inherent difficulties in these tasks stem from Multi-word Expressions (MWEs), which complicate the tasks by introducing ambiguity, idiomatic expressions, infrequent usage, and a wide range of variations. Significant effort and substantial progress have been made in addressing the challenging nature of MWEs in Western languages, particularly English. This progress is attributed in part to the well-established research communities and the abundant availability of computational resources. However, the same level of progress is not true for language families such as Chinese and closely related Asian languages, which continue to lag behind in this regard. While sub-word modelling has been successfully applied to many Western languages to address rare words improving phrase comprehension, and enhancing machine translation (MT) through techniques like byte-pair encoding (BPE), it cannot be applied directly to ideograph language scripts like Chinese. In this work, we conduct a systematic study of the Chinese character decomposition technology in the context of MWE-aware neural machine translation (NMT). Furthermore, we report experiments to examine how Chinese character decomposition technology contributes to the representation of the original meanings of Chinese words and characters, and how it can effectively address the challenges of translating MWEs.