The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation

Adam Meyers, Rodolfo Joel Zevallos, John E. Ortega, Lisa Wang


Abstract
Translating between languages with drastically different grammatical conventions poses significant 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 ‘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.
Anthology ID:
2025.bucc-1.7
Volume:
Proceedings of the 18th Workshop on Building and Using Comparable Corpora (BUCC)
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Serge Sharoff, Ayla Rigouts Terryn, Pierre Zweigenbaum, Reinhard Rapp
Venues:
BUCC | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
57–61
Language:
URL:
https://aclanthology.org/2025.bucc-1.7/
DOI:
Bibkey:
Cite (ACL):
Adam Meyers, Rodolfo Joel Zevallos, John E. Ortega, and Lisa Wang. 2025. The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation. In Proceedings of the 18th Workshop on Building and Using Comparable Corpora (BUCC), pages 57–61, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation (Meyers et al., BUCC 2025)
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PDF:
https://aclanthology.org/2025.bucc-1.7.pdf