Note: [**] Work conducted while both authors were affiliated with the Universitat Politècnica de València.
Abstract: Machine Translation (MT) requires a large amount of linguistic resources, which leads current MT systems to leaving unknown words untranslated. This can be annoying for end users, as they might not understand at all such untranslated words. However, most language families share a common vocabulary, therefore this knowledge can be leveraged to produce more understandable translations, typically for “assimilation” or gisting use. Based on this observation, we propose a method that constructs polyglot translations tailored to a particular user language. Simply put, an unknown word is translated into a set of languages that relate to the user’s language, and the translated word that is closest to the user’s language is used as a replacement of the unknown word. Experimental results on language coverage over three language families indicate that our method may improve the usefulness of MT systems. As confirmed by a subsequent human evaluation, polyglot translations look indeed familiar to the users, and are perceived to be easier to read and understand than translations in their related natural languages.
Keywords: Minority languages, machine translation, linguistic coverage, vocabulary, human factors
