wubet/concerted-training-nmt-amharic

# Amharic-English Concerted training NMT The Amharic-English Concerted training NMT (Neural Machine Translation) architecture is developed based on the "Concerted Training Neural Machine Translation (CTNMT)" architecture, which incorporates novel techniques such as "dynamic switch" and "rate schedule" in its training methodology. Moreover, it enhances NMT translation effectiveness by incorporating insights from BERT (Bidirectional Encoder Representations from Transformers), a groundbreaking pre-training model. Here's a simplified breakdown of the components involved: 1. Neural Machine Translation (NMT): This method employs extensive neural networks for machine translation, significantly enhancing translation quality by processing entire texts as cohesive units. This advancement results in translations that are both more fluent and accurate. 2. BERT Pre-training Model: BERT revolutionizes the way language representations are pre-trained, excelling in a variety of natural language processing (NLP) tasks. It models the context surrounding each word by considering the surrounding words, diverging from older models that could only process text in a single direction. Utilizing BERT within NMT frameworks can greatly improve the grasp of linguistic subtleties, thereby enhancing translation accuracy. The architecture integrates BERT's pre-trained language understanding with NMT aims to provide a more nuanced understanding of both source and target languages. This approach is beneficial for languages with less digital resources, like Amharic, by boosting the model's ability to understand and translate contextually rich and complex sentences. The architecture's design emphasizes the harmonization of various training elements to refine the translation workflow. It includes: • Dynamic Switch: This refers to a method where the training process dynamically switches between different modes or focuses. For instance, it might alternate between focusing on learning from contex …