Summary of Key Ingredients For Effective Zero-shot Cross-lingual Knowledge Transfer in Generative Tasks, by Nadezhda Chirkova et al.
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks
by Nadezhda Chirkova, Vassilina Nikoulina
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates zero-shot cross-lingual knowledge transfer in natural language processing, specifically in text generation tasks. It focuses on finetuning multilingual pretrained language models to generate text in languages other than the one they were originally trained on. The study compares various approaches proposed in the literature, including different backbone models and learning rate tuning strategies. The results show that careful learning rate tuning is crucial for alleviating the problem of generating text in the wrong language, and that simple full finetuning can be a strong baseline. The paper also finds that mBART performs similarly to mT5 and NLLB-200 can be competitive in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can learn to generate text in different languages without being taught specifically for each language. It’s like teaching a child to speak multiple languages just by showing them how to say a few words in one language! The researchers tested different ways to make this happen and found that some methods work better than others. They also compared different “backbone” models, which are like the foundation of a building. Surprisingly, they found that using a simple approach can be really good at generating text in other languages! |
Keywords
» Artificial intelligence » Natural language processing » Text generation » Zero shot
