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Can cross-lingual models improve machine translation in low-resource languages?

Benton Urling

Definitely, cross-lingual models are a powerful tool to enhance machine translation for low-resource languages. Currently, most of the machine translation systems require a vast amount of bilingual data to provide accurate translations. In low-resourced languages, the scarcity of parallel corpora makes it hard to create quality models. Nevertheless, cross-lingual models may help in improving machine translation performances under these conditions.

Cross-lingual models provide a way to transfer knowledge from high-resource languages to low-resource ones and alleviate the need for large amounts of training data. These models work by learning a mapping between different languages; therefore, the language pair doesn't need to be explicitly paired. The explanatory power of cross-lingual models lies behind the fact that many languages share similar syntactic and semantic patterns. Therefore, a model trained on a high-resource language could learn these patterns and generalize the knowledge to the low-resource languages.

The use of cross-lingual models in machine translation still needs to be fully exploited. Nevertheless, there are already some successful examples. For instance, Google's neural machine translation system has achieved significant improvements in low-resource languages such as Swahili, Turkish, and Indonesian, thanks to the use of cross-lingual models. Similarly, Facebook's M2M-100 model, which can translate between 100 different languages, is also based on cross-lingual models.

Recent research has shown that with the use of cross-lingual models, high performing machine translation models for low-resource languages can be created. A study conducted on English-Swahili machine translation showed that the implementation of cross-lingual models increased the translation quality by a large margin, compared to systems that do not use such models. Furthermore, research carried out on Nepali-English machine translation also demonstrated that the use of cross-lingual models increases the quality of the translations.

Despite the benefits brought by cross-lingual models, several challenges should be taken into consideration. First, it's essential to ensure that the model's transfer learns the correct mappings between the languages. If the cross-lingual models don't learn the correct alignment, the translation quality might be worse than the systems that used the same amount of bilingual data. Second, it's important to emphasize that cross-lingual models' translation quality for low-resource languages depends on how well the models match the data. Third, some low-resource languages have linguistically complex structures and use rare characters or scripts, which make it harder for cross-lingual models to learn an accurate mapping.

In conclusion, cross-lingual models have shown their potential in improving machine translation in low-resource languages, where bilingual data is scarce. The use of these models will continue to grow as more research advances in artificial intelligence, and the potential of cross-lingual models is further explored. Nevertheless, the benefits of cross-lingual models should also be considered with a realistic perspective of the specific language, data, and required application.