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

TL;DR

This paper improves Chinese-to-English machine translation by specifically addressing the handling of attributive nouns and the omitted particle 'DE', leading to more accurate translations in news titles.

Contribution

It introduces a targeted dataset and fine-tuning approach to better handle attributive nouns and omitted particles in Chinese-English translation.

Findings

Enhanced translation accuracy for attributive nouns

Reduced errors related to omitted particles

Practical improvement in news title translation

Abstract

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.