What is neural machine translation?

by Stephen M. Walker II, Co-Founder / CEO

What is neural machine translation?

Neural Machine Translation (NMT) is a state-of-the-art machine translation approach that uses artificial neural network techniques to predict the likelihood of a sequence of words. This can be a text fragment, a complete sentence, or even an entire document with the latest advances. NMT is a form of end-to-end learning that can be used to automatically produce translations.

NMT systems typically consist of two main sections: an encoder network and a decoder network. Both are neural networks. The encoder network processes the input text and the decoder network generates the translated text. Each word in the input sentence is encoded as a number to be translated by the neural network.

NMT has quickly become the dominant approach to machine translation, producing translations that rival human translations in quality. It typically produces much higher quality translations than Statistical Machine Translation (SMT), with better fluency and adequacy. However, there still remain challenges, especially with languages where less high-quality data is available, and with domain shift between the data a system was trained on and the texts it is supposed to translate.

One of the key benefits of NMT is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning. This makes NMT a more integrated and efficient approach to machine translation. However, it's worth noting that NMT systems can sometimes struggle with consistency, as they are trained to learn from large volumes of data and are not easy to control.

The future of NMT is very promising, with its capabilities expected to grow over time as artificial intelligence continues to evolve. However, it's important to note that there are certain types of content and communication styles that NMT has difficulty with, and human translators still excel at translating these kinds of content.

Understanding NMT

Despite its infancy, NMT has shown potential in translating between vastly different languages. It outperforms traditional methods by considering sentence context and grammar, translating multiple sentences simultaneously, and reducing the need for human intervention. These advancements lead to benefits such as higher translation accuracy, speed, efficiency, flexibility, and scalability.

However, NMT faces challenges like the need for extensive training data and the scarcity of parallel data for many language pairs. Ambiguity and idiomatic expressions also pose difficulties, as NMT systems may default to literal translations. Additionally, achieving fluency in output remains a hurdle, with translations sometimes sounding unnatural.

The future of NMT is promising, with ongoing research aimed at enhancing system performance. As technology progresses and more data becomes available, NMT is expected to set new standards in machine translation, significantly improving global communication.

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