Summary of How Far Can 100 Samples Go? Unlocking Overall Zero-shot Multilingual Translation Via Tiny Multi-parallel Data, by Di Wu et al.
How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data
by Di Wu, Shaomu Tan, Yan Meng, David Stap, Christof Monz
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the long-standing challenge of zero-shot translation in Multilingual Machine Translation (MMT), where a model must translate between language pairs unseen during training. The authors propose an innovative approach that surprisingly achieves significant improvements by fine-tuning with a small amount of multi-parallel data, rather than relying on a large and resource-intensive training corpus. For instance, the EC30 dataset sees up to +21.7 ChrF non-English overall improvements (870 directions) when using just 100 multi-parallel samples while maintaining English-centric translation quality. The study also examines the size effect of fine-tuning data and its transfer capabilities, revealing that a small, randomly sampled set of fine-tuning directions is sufficient to achieve comparable improvements. Even with minimal settings – such as fine-tuning with only one sample – the well-known off-target issue is nearly resolved, partly explaining the observed enhancements in translation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Zero-shot translation is like trying to translate languages you’ve never seen before! This paper shows that by using a tiny bit of extra information, we can make machines better at translating between these languages. It’s like a puzzle piece that helps fit together words and meanings from different languages. The researchers tested this idea and found that it really works – even when using just one example or very little extra information! |
Keywords
* Artificial intelligence * Fine tuning * Translation * Zero shot
