They use a pragmatic approach, leveraging all parallel data available for higher resource languages to improve the quality of long-tail languages with only monolingual. With monolingual data collected from the web, the next issue is to develop high-quality, general-domain MT models from small amounts of monolingual training data. The first is data scarcity digitized material for many languages is scarce, and finding it on the web can be difficult due to quality concerns with Language Identification (LangID) models. Furthermore, the covered languages are predominantly European, generally ignoring linguistically diverse regions such as Africa and the Americas.īuilding functional translation models for the long tail of languages face two major roadblocks. Even though existing translation services cover languages spoken by most people throughout the world, they only cover about 100 languages, accounting for slightly over 1% of all languages spoken globally. The quality of translation services has increased, and they have grown to incorporate new languages, according to research benchmarks such as WMT. Machine translation (MT) technology has advanced significantly. Please Don't Forget To Join Our ML Subreddit All Credit For This Research Goes To The Researchers 👏👏👏

6 Shares This Article Is Based On The Research Paper ' BUILDING MACHINE TRANSLATION SYSTEMS FOR THE NEXT THOUSAND LANGUAGES'.

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