LANGUAGE IDENTIFICATION FOR MULTILINGUAL MACHINE

Abstract

Language Identification for Multilingual Machine Translation is a crucial component in modern natural language processing systems, enabling accurate and efficient translation across multiple languages. This paper presents a comprehensive approach to language identification that enhances the performance of multilingual machine translation systems.The proposed method utilizes advanced machine learning techniques to automatically detect the language of a given text with high accuracy. By incorporating a variety of linguistic features and leveraging large-scale multilingual datasets, the system can identify languages even in challenging scenarios such as code-switching and mixed-language inputs.Key contributions of this work include the development of a robust language identification model that integrates seamlessly with machine translation pipelines. The model is evaluated on diverse datasets, demonstrating its effectiveness in real-world applications. Additionally, we explore the impact of accurate language identification on the overall quality of machine translation, highlighting improvements in translation accuracy and fluency.