Summary of Empirical Study Of Pretrained Multilingual Language Models For Zero-shot Cross-lingual Knowledge Transfer in Generation, by Nadezhda Chirkova et al.
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation
by Nadezhda Chirkova, Sheng Liang, Vassilina Nikoulina
First submitted to arxiv on: 15 Oct 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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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 research paper explores the application of zero-shot cross-lingual knowledge transfer to multilingual pretrained language models (mPLMs) in the context of text generation. The study focuses on evaluating alternative mPLMs, such as mBART and NLLB-200, for their ability to perform tasks in various languages without requiring explicit training data. The results show that mBART with adapters can achieve similar performance to mT5 of the same size, while NLLB-200 can be competitive in certain scenarios. Furthermore, the paper highlights the importance of tuning the learning rate used during finetuning to alleviate issues related to generating text in the wrong language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can learn new skills without being taught directly for each language they need to work with. It’s like a person who learned a new language and can now communicate with people who speak different languages. The researchers tested special computer models called mBART and NLLB-200 to see if they could help machines understand and generate text in different languages. They found that some of these models worked well, while others needed adjustments. This is important because it helps us build better computers that can communicate with people who speak different languages. |
Keywords
» Artificial intelligence » Text generation » Zero shot
