Reducing Redundancy in Japanese-to-English Translation: A Multi-Pipeline Approach for Translating Repeated Elements in Japanese
Qiao Wang, Yixuan Huang, Zheng Peng Yuan · 2024
This paper presents a multi-pipeline Japanese-to-English machine translation (MT) system designed to address the challenge of translating repeated elements from Japanese into fluent and lexically diverse English.The system was developed as part of the Non-Repetitive Translation Task at WMT24, which focuses on minimizing redundancy while maintaining high translation quality.Our approach utilizes MeCab, the de facto Natural Language Processing (NLP) tool for Japanese, to identify repeated elements, and Claude Sonnet 3.5, a Large Language Model (LLM), for translation and proofreading.The system effectively accomplishes the shared task by identifying and translating in a diversified manner 89.79% of the 470 repeated instances in the test dataset and achieving an average translation quality score of 4.60 out of 5, significantly surpassing the baseline score of 3.88.The analysis also revealed challenges, particularly in identifying standalone noun-suffix elements and occasional cases of consistent translations or mistranslations.