ADVANCEMENTS IN BERT MODELS AND ALGORITHMS FOR DEVELOPING TRANSLATION SOFTWARE
Keywords:
BERT models, machine translation, transformer architecture, bidirectional context, computational resources, translation algorithms, hybrid models, translation softwareAbstract
This article explores the advancements in BERT (Bidirectional Encoder Representations from Transformers) models and algorithms for the development of translation software. It discusses how BERT's bidirectional context understanding and Transformer architecture have significantly improved machine translation (MT) systems. The article highlights key innovations such as multilingual BERT (mBERT), cross-lingual transfer learning, and sentence-level attention mechanisms, which enhance translation accuracy and fluency. Additionally, it examines the challenges faced by BERT-based systems, including computational cost and language coverage, while also considering future advancements in hybrid models and unsupervised learning techniques. Overall, the article emphasizes the ongoing potential of BERT-based models in transforming translation software and driving the next generation of machine translation.
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