Little over a week ago, Gemini 1.5 reported close to human-level performance on MTOB, a recent challenging translation dataset. In this post, we’ll dig into this result, explore true zero-shot machine translation (MT), and consider how to teach LLMs a new language like humans.
To set the scene, let’s first consider what it means for a language to be considered “low-resource”. As with LLMs, the performance of MT models depends on the amount of training data—both parallel and monolingual—available in a given language. As a result, there is a gulf between languages with lots of data and languages with little data in common pre-training corpora. The latter are typically referred to as “low-resource”.
To bridge the gap between resource-rich and resource-poor languages and make machine translation more accessible, new translation benchmarks have been created that cater specifically to low-resource languages. The Conference on Machine Translation (WMT) now regularly hosts shared tasks on low-resource MT such as for Indic and African languages; workshops such as AmericasNLP support indigenous languages; and large-scale decentralized collaborations such as Masakhane, SEACrowd and Aya created MT datasets for African languages, Indonesian languages, and 100+ languages respectively. Recently, FLORES-200 expands translation data coverage to 200 languages. Beyond theses efforts, through extensive work on data cleaning, filtering, and language identification, researchers have been able to obtain data and train MT models for 1000+ languages (Bapna et al., 2022; NLLB Team, 2022).
Source: Sebastian Ruder — Read the full article on the original site ↗