Whereas RNNs gather information from each part of the sentence, thus making the process computationally too expensive and creating a bottleneck for longer sentences. This approach allows the system to translate very long sentences without losing context and quality. While other machine translation processes already used artificial neural networks, the real innovation was introducing the so-called “ attention mechanism” or Transformer. The recurrent neural networks (the particular form of neural networks used to encode and decode the sentences) were able to “understand” the context way better and to give a much more fluent translation. The first part was abandoning the statistical paradigm, which was still bound to the word-for-word translation and lacking context. It was the birth of Google Neural Machine Translation (GNMT).Īctually, it was a double leap. Google Translate was launched in 2006, but only a decade later did Google make the most significant leap in the recent history of commercial MT-by switching from a statistical, phrase-based to a neural machine translation system. There’s hardly anyone who hasn’t heard of it: Google Translate has been incorporated in almost every product of the Google ecosystem and has reached high-quality levels-not only for the most common language pairs but also for less popular ones. That’s why we’ve picked 5 engines in the vast machine translation services market, based on translation quality and technological features, to help users get a strong overview and identify the most suitable MT provider for their translation needs. ![]() ![]() It all comes down to the specific needs of the user in terms of content type, subject matter, and target audience. Put another way, there is no perfect machine translation technology-nor the perfect MT engine. Even the widespread opinion that an NMT system would offer the highest quality translations isn’t entirely true-it has its downsides as well. So, the question is now: What is the best machine translation software on the market? And most importantly: What are the differences among the many online machine translation engines available? The first answer might be easy: There are no absolute best tools, engines, or providers. With this innovation potential, global tech giants have been increasingly disrupting the language industry year by year. Thanks to neural machine translation (NMT) algorithms can now generate such accurate translations that they’re way better than just “good enough.” Ever since Google brought about the “ MT neural revolution” in 2016, machine translation results have significantly improved in terms of quality, consistency, and productivity. In the early stages of applying machine learning to language translation, machine translation was primarily used to get a rough translation of a text. The advancement in MT translation software ![]()
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