NLP

Introduction: Machine translation, the process of automatically translating text from one language to another, has witnessed significant advancements in recent years. Neural Machine Translation (NMT) has emerged as a revolutionary approach that has significantly improved the quality of translations compared to traditional rule-based and statistical methods. NMT uses deep learning models to translate entire sentences or paragraphs, capturing complex linguistic patterns and context. In this blog, we will delve into the challenges faced by machine translation, explore the techniques used in neural machine translation, examine its limitations, and discuss potential future work to further enhance this cutting-edge technology. Techniques in Neural Machine Translation 1. Sequence-to-Sequence (Seq2Seq) Models: The core of NMT lies in Seq2Seq models, consisting of an encoder-decoder architecture. The encoder processes the input sentence and converts it into a fixed-length vector representation ...