Language Identification for Seamless Multilingual Machine Translation

Language identification is the measurement in any language translation from one language to another, it is at the center of any such multilingual machine applications. This paper presents the current technologies that enhance language identification in improving both human and machine translations. The proposed work strongly supports the automatic recognition of the specific language of text with high accuracy and high degree of utility based on varied and complex machine learning paradigms using the classifiers are KNN, SVM and random forest. Moreover, the system really ought to be able to detect normal and code-switched languages as a function of various sorts of multilingual data and different linguistic contexts stemming from environments whereby these forced interaction take place. This is a unique feature of the integrated language identification model added to the machine translation pipeline. The entire evaluation strategy has been performed over varying datasets, proving time-efficient in the proposed method. The proposed methods shows the quality of language identification drastically influences the general quality of the resulting translations in machine translation, it improves translation accuracy and fluency with 86.88%.