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5 days ago · Role of Deep Learning in AI Translation Technology. Deep learning is a subset of machine learning that focuses on training artificial neural networks to perform complex tasks. In the context of AI translation technology, deep learning algorithms are used to train neural networks on vast amounts of multilingual data.
4 days ago · True, the approach of prompting-based machine translation using large language models (LLMs) offers more natural and contextually relevant translations than NMT, especially when domain-specific training data is limited or non-existent. LLM translation is great for Japanese <> Spanish or Polish <> Mandarin, off-the-cuff.
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4 days ago · Neural Machine Translation. The most recent advancement in machine translation is the introduction of Neural Machine Translation (NMT) systems. These systems use deep learning techniques to model the entire translation process. NMT systems have significantly improved the quality of machine translation, making it more fluent and accurate.
13 hours ago · Bahdanau et al. introduced an attention mechanism into an encoder-decoder neural machine translation architecture based on recurrent neural networks (RNNs). The innovation of the attention mechanism lies in its ability to enable a decoder to selectively focus on specific parts of an input sentence when translating the next word.
3 days ago · The Mistral-7B-v0.1, a Large Language Model (LLM) with 7 billion parameters, outperforms benchmarks like Llama 2 13B across domains. It utilizes transformer architecture with specific attention mechanisms and a Byte-fallback BPE tokenizer. It excels in text generation, natural language understanding, language translation, and serves as a base ...
5 days ago · Also known as sequence transduction, neural machine translation describes any task where an input sequence is translated into an output sequence. However, for a transformer to perform this translation, it must possess some form of memory.
5 days ago · Galaxy AI features such as Live Translate perform three core processes: automatic speech recognition (ASR), neural machine translation (NMT) and text-to-speech (TTS). Each process needs a distinct set of information. ASR, for instance, needs extensive recordings of speech in numerous environments, each paired with an accurate text transcription.
