Summary of On the Evaluation Practices in Multilingual Nlp: Can Machine Translation Offer An Alternative to Human Translations?, by Rochelle Choenni et al.
On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?
by Rochelle Choenni, Sara Rajaee, Christof Monz, Ekaterina Shutova
First submitted to arxiv on: 20 Jun 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 The proposed paper presents an analysis of existing evaluation frameworks in multilingual NLP, discussing their limitations and proposing directions for more robust and reliable evaluation practices. The authors empirically study the use of machine translation as a reliable alternative to human translation for large-scale evaluation of MLMs across a wide set of languages. They translate test data from 4 tasks into 198 languages using a SOTA translation model and evaluate three MLMs, showing that while subsets of high-resource test languages are generally representative, there is a tendency to overestimate performance on low-resource languages. Finally, the authors demonstrate that simpler baselines can achieve strong performance without large-scale multilingual pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we evaluate language models that can understand many languages. Right now, we only test these models on a few languages because it’s hard to find data for all languages. This is a problem when we want to know how well the model will work in languages it has never seen before. The authors of this paper think about what’s wrong with current evaluation methods and suggest new ways to make them more reliable. They also test how well machine translation works as an alternative way to evaluate these models. They translate text from 4 tasks into many languages (198!) and check how three language models do on this task. The results show that we tend to think the models are better at understanding low-resource languages than they actually are. Finally, the authors show that simpler methods can work well without needing all the training data. |
Keywords
* Artificial intelligence * Nlp * Pretraining * Translation
