LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages

Knowing the language of an input text/audio is a necessary first step for using almost every natural language processing (NLP) tool such as taggers, parsers, or translation systems. Language identification is a well-studied problem, sometimes even considered solved; in reality, most of the world's 7000 languages are not supported by current systems. This lack of representation affects large-scale data mining efforts and further exacerbates data shortage for low-resource languages. We take a step towards tackling the data bottleneck by compiling a corpus of over 50K parallel children's stories in 350+ languages and dialects, and the computation bottleneck by building lightweight hierarchical models for language identification. Our data can serve as benchmark data for language identification of short texts and for understudied translation directions such as those between Indian or African languages. Our proposed method, Hierarchical LIMIT, uses limited computation to expand coverage into excluded languages while maintaining prediction quality.