Summary of Investigating the Potential Of Sparse Mixtures-of-experts For Multi-domain Neural Machine Translation, by Nadezhda Chirkova et al.
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation
by Nadezhda Chirkova, Vassilina Nikoulina, Jean-Luc Meunier, Alexandre Bérard
First submitted to arxiv on: 1 Jul 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 explores efficient neural machine translation models that can handle data from various domains seen during training and are robust to unseen domains. The authors hypothesize that Sparse Mixture-of-Experts (SMoE) models are well-suited for this task due to their ability to scale efficiently and share parameters between domains, potentially enabling knowledge transfer and limiting negative transfer. The authors conduct experiments validating the utility of SMoE for multi-domain scenarios and find that a straightforward width scaling of Transformer is a simpler and more efficient approach in practice, reaching similar performance levels as SMoE. Additionally, the paper highlights the importance of mixing-in a generic domain (Paracrawl) and introduces a simple technique called domain randomization to improve robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine translation models work well with data from different areas or topics. The authors think that special models called Sparse Mixture-of-Experts (SMoE) are good for this because they can handle many types of data and share information between them. They tested these models and found that a simpler way, using something called Transformer, works just as well. The paper also says it’s important to include some extra data from the internet (Paracrawl) and has an easy technique called domain randomization to help make the model better. |
Keywords
» Artificial intelligence » Mixture of experts » Transformer » Translation
