The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation
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Abstract (arXiv)
Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation. By manually inserting the omitted particle X ('DE'). In news article titles from the Penn Chinese Discourse Treebank, we developed a targeted dataset to fine-tune Hugging Face Chinese to English translation models, specifically improving how this critical function word is handled. This focused approach not only complements the broader strategies suggested by previous studies but also offers a practical enhancement by specifically addressing a common error type in Chinese-English translation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 18 Dec 2024.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
- 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
- 2018Marian: Fast Neural Machine Translation in C++cited by 203
- 2021MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairscited by 41
- 2019Towards Understanding Neural Machine Translation with Word Importancecited by 19
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