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arXiv CS.AI·1d ago·5 min read
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs
TL;DR
Back-translation (BT) significantly enhances machine translation for low-resource languages by utilizing monolingual data, outperforming traditional methods. In tests, BT improved translation quality by up to 173% compared to parallel-only fine-tuning.
✦ Why It Matters
Implement back-translation techniques to enhance translation quality for low-resource languages in your projects today.
Key Takeaways
- Utilize back-translation for low-resource language translation tasks.
- Incorporate monolingual data to improve translation models.
- Experiment with beam-decoded translations for better results.
- Evaluate translation quality using both automatic metrics and human assessments.
How It Works
Back-translation involves translating monolingual sentences from one language to another, creating synthetic parallel data. This data is then used to train the model in the reverse direction, effectively enhancing its translation capabilities.
By interleaving updates from a small parallel dataset, the model achieves stable learning and improved performance.