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One Sentence Abstract

This study examines massively multilingual neural machine translation (NMT) models that effectively translate up to 102 languages to and from English, outperforming previous state-of-the-art models and showcasing promising results in low resource settings.

Simplified Abstract

This research focuses on improving the way machines can translate between multiple languages. Instead of training separate models for each language pair, the study investigates a single model that can translate from many languages to many others. In this case, they train a model to translate up to 102 languages at once, which is a significant increase.

To achieve this, they test various methods and compare the results. They find that the new "massively multilingual" approach works well, even with limited resources, and is better than previous methods for translating up to 59 languages. They also test the method with a large-scale dataset of 102 languages and get impressive results, performing better than traditional bilingual models.

This study is important because it helps us understand how countries collaborate in science by looking at the translation of their research. By creating a single model that can translate between many languages, the researchers have developed a more accurate and reliable method that can be applied to a wide range of languages, making it easier for scientists around the world to share their work and collaborate.

Study Fields

Main fields:

• Natural Language Processing (NLP)
• Neural Machine Translation (NMT)
• Multilingualism

Subfields:

• Massively multilingual NMT
• Modeling decisions and trade-offs
• Translation quality
• Low-resource settings
• Large-scale dataset analysis
• Performance comparison with bilingual baselines

Study Objectives

• Investigate the limits of multilingual Neural Machine Translation (NMT) in terms of the number of languages supported
• Train massively multilingual NMT models to translate up to 102 languages to and from English within a single model
• Explore different setups for training such models and analyze the trade-offs between translation quality and various modeling decisions
• Evaluate the performance of massively multilingual many-to-many models in low resource settings using the publicly available TED talks multilingual corpus
• Demonstrate the effectiveness of massively multilingual many-to-many models by outperforming the previous state-of-the-art while supporting up to 59 languages
• Conduct experiments on a large-scale dataset with 102 languages to and from English and up to one million examples per direction to show promising results and encourage future work on massively multilingual NMT

Conclusions

• The study demonstrates the effectiveness of massively multilingual many-to-many neural machine translation (NMT) models, training a single model to support translation from multiple source languages to multiple target languages.
• The authors show that these models can be effective in low resource settings, outperforming the previous state-of-the-art by supporting up to 59 languages.
• They perform experiments on a large-scale dataset with 102 languages to and from English and up to one million examples per direction, which result in promising outcomes, surpassing strong bilingual baselines.
• The study explores different setups for training such models and analyzes the trade-offs between translation quality and various modeling decisions.
• The results encourage further research on massively multilingual NMT and its potential applications in various language translation tasks.

References