Optimizing Cross Language Information Retrieval and Context Translation Using BERT Model and Deep Learning Techniques

This study proposes a strategy that combines the Bidirectional Encoder Representations from Transformers (BERT) model with an attention-based Seq2Seq learning framework to improve cross-linguistic information retrieval and contextual translation. The BERT model is pre-trained on large-scale multilingual corpora to capture universal semantic representations, which enhances the model's ability to understand complex contexts across different languages. The pre-trained BERT model is then integrated with the Seq2Seq framework and fine-tuned on a bilingual parallel corpus to optimize performance in cross-lingual translation and retrieval tasks. Additionally, hyperparameters are fine-tuned, and targeted optimization strategies are introduced for low-resource languages to further enhance the model's applicability. Experimental results demonstrate that the proposed method achieves a BLEU score of 0.79 after 20 epochs, an NDCG score of 0.75 for 10 queries, and performs well in low-resource language processing. The BERT+Seq2Seq model shows strong generalization and adaptability, making it one of the most effective methods for cross-linguistic processing.