Summary of Disentangling the Roles Of Target-side Transfer and Regularization in Multilingual Machine Translation, by Yan Meng and Christof Monz
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation
by Yan Meng, Christof Monz
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 investigates the dynamics of knowledge transfer in multilingual machine translation (MMT) when varying the auxiliary target side languages along two dimensions: linguistic similarity and corpus size. The study reveals that linguistically similar auxiliary languages exhibit strong positive transfer ability, which enhances translation performance for main language pairs as corpus size increases. Interestingly, distant auxiliary languages can also benefit main language pairs with minimal positive transfer, acting as a regularizer to improve generalization and model inference calibration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning helps translate words from one language into another. They found that when using similar languages to help, it really improves the translation quality! But what’s even cooler is that they discovered that using completely different languages can also make things better – just a little bit! It’s like having a magic tool that makes your translations more accurate and smart. |
Keywords
* Artificial intelligence * Generalization * Inference * Machine learning * Translation
