Language Identification For Multilingual Machine Translation

Abstract

In today's globalized digital world, multilingual
machine translation systems are becoming
increasingly important to facilitate
communication across diverse languages. A
fundamental component of these systems is
language identification, which accurately detects
the source language before translation can occur.
Effective language identification ensures that the
correct translation models are applied, improving
the quality, speed, and reliability of multilingual
communication. Given the increasing complexity
of user inputs—such as code-mixed, noisy, or
low-resource language data—building robust
language identification modules is critical for
enhancing machine translation performance in
real-world applications.
This project explores advanced techniques for
language identification, including deep learning
models, character-level embeddings, and
statistical methods, to classify the input language
accurately in multilingual settings. We address
challenges like short text classification, language
similarity, and the presence of mixed-language
content. By integrating a highly accurate
language identifier within a multilingual machine
translation pipeline, the system can dynamically
route inputs to the most suitable translation
engine, thereby optimizing translation accuracy
and user satisfaction. The proposed approach not
only strengthens the overall translation workflow
but also sets the foundation for building more
inclusive and accessible communication
technologies.