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Japanese-Chinese Translation with GenAI: What Works and What Doesn’t
- Translating high-context languages, like Chinese and Japanese, presents unique challenges due to the importance of context, culture, and history in these languages.
- Traditional translation tools like Google Translate and DeepL faced issues with accuracy, but Gen AI has shown significant improvement in translation quality.
- The article documents the testing of 10 Gen AI models for Chinese-Japanese translation, providing insights and tips for enhancing translation quality.
- Challenges identified include inconsistent translations, pronoun overuse, incorrect pronoun usage, mix of Kanji, Simplified Chinese, and Traditional Chinese, and punctuation differences.
- Testing criteria involved evaluating pronoun errors, non-Chinese character usage, and pronoun addition rates to quantify the translation quality of different models.
- Applying translation guidance significantly improved overall translation quality, showcasing models like Claude-3.5 Sonnet and OpenAI GPT-4o as top performers.
- Factors like budget, response time, ecosystem compatibility, and model size influence the selection of Gen AI models for English-Chinese-Japanese translation.
- The study acknowledges limitations in testing and highlights the need for further improvements in AI translation for non-English languages like Japanese and Chinese.
- Challenges including cost considerations, the need for detailed prompts for accurate translation, and the push for improved contextual understanding and cultural awareness in AI translation.
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