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Minimum Bayes Risk decoding is a practical technique for selecting better translations in neural machine translation systems. This video explains how MBR decoding works, why it performs better than greedy decoding and random sampling, and how similarity metrics like ROUGE are used to find a consensus translation. 👍 Like | 💬 Comment | 🔔 Subscribe for more NLP Videos The video walks through the core idea behind Minimum Bayes Risk decoding in simple terms. You will see how multiple candidate translations are compared against each other and how the most representative output is selected based on average similarity. This tutorial is designed for NLP learners, machine learning engineers, and researchers working with neural machine translation models. Concepts are explained clearly without unnecessary theory, making it suitable for beginners while still useful for practitioners. The video also includes a simple Python example using a built in dataset to demonstrate how pairwise text similarity and averaging work. This helps connect the theory behind MBR decoding with practical implementation ideas. Topics covered include neural machine translation decoding strategies, Minimum Bayes Risk decoding, ROUGE based similarity comparison, evaluation of translation candidates, and practical considerations for NMT systems. If you are working on machine translation models or studying NLP evaluation techniques, this video provides a clear and realistic explanation of how MBR decoding fits into modern NMT pipelines. 💬 Follow & Connect GitHub Repository:https://github.com/dr-mushtaq/natural... Enroll Full Course: https://coursesteach.com/ Whatsapp Group:https://chat.whatsapp.com/L9URPRThBEa... #MinimumBayesRisk #MBRDecoding NeuralMachineTranslation #NLP #MachineTranslation #NLPResearch #DeepLearning #AIResearch #TextGeneration #ROUGEScore #NLPModels #AIEngineering