A Hybrid Deep Learning Approach for Efficient Cross-Language Detection

Cross-language detection is a challenging task that involves identifying the language of a given text across multiple languages, often in noisy or mixed-language environments. This also identify and classify text across different languages for various applications, such as multilingual sentiment analysis, language translation and cross-border content moderations. Traditional approaches often rely on rule-based systems or monolingual models, which lack scalability and adaptability to diverse linguistic structures. In this study, we propose a hybrid deep learning model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to enhance language detection accuracy and robustness. LSTM and GRU, known for their ability to capture long-term dependencies and reduce vanishing gradient problems, are integrated to leverage their complementary strengths. The model is evaluated using BLEU scores, a widely accepted metric for evaluating linguistic quality, and perplexity, which measures the model's ability to predict a sequence of words. Our experimental results demonstrate that the hybrid deep learning model outperforms traditional approaches, achieving high BLEU scores and low perplexity across diverse multilingual datasets. This approach not only improves language detection accuracy but also reduces computational complexity, making it suitable for real-time applications in multilingual text processing. The proposed model shows promise in real-world applications, enabling efficient cross-language detection in multilingual environments.