Summary of Low-resource Machine Translation Through the Lens Of Personalized Federated Learning, by Viktor Moskvoretskii et al.
Low-Resource Machine Translation through the Lens of Personalized Federated Learning
by Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horváth, Eduard Gorbunov, Irina Nikishina
First submitted to arxiv on: 18 Jun 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 A new approach called MeritOpt, based on Personalized Federated Learning algorithm MeritFed, is introduced for Natural Language Tasks with heterogeneous data. This method, applied to the Low-Resource Machine Translation task using South East Asian and Finno-Ugric language datasets, demonstrates effectiveness and interpretability, enabling tracking of each language’s impact during training. Analysis reveals that target dataset size affects weight distribution across auxiliary languages, unrelated languages do not interfere with training, and auxiliary optimizer parameters have minimal impact. MeritOpt is easy to apply, requiring only a few lines of code, and scripts for reproducing experiments are provided at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MeritOpt is a new way to learn about different languages using computers. It’s based on an old algorithm called MeritFed, but it works better with language tasks that have different types of data. We tested it with machine translation from Asian and Finno-Ugric languages. The results show that this method is good at understanding language relationships and can even track which languages are most important for learning. This approach is easy to use and only needs a few lines of code. |
Keywords
» Artificial intelligence » Federated learning » Tracking » Translation
