✨Links to Neural Machine Translation

https://translab.lv/en/machine-translation-smt-vs-nmt/

Machine translation (SMT VS NMT) • Translab

Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) are both methods of machine translation, but they differ in how they model the relationship between the source and target languages.

SMT is a rule-based method that uses statistical models to translate text. It relies on a large parallel corpus of source and target language sentences to learn how to translate. The main idea behind SMT is to find patterns and rules in the parallel corpus that can be used to translate new sentences.

NMT, on the other hand, is a data-driven method that uses neural networks to model the relationship between the source and target languages. It relies on large parallel corpora of source and target sentences to train a neural network to translate text. The main idea behind NMT is to represent the meaning of a text as a set of numerical values, known as embeddings, which can be used as input to a neural network.

The main difference between SMT and NMT is that SMT is based on rule-based methods, whereas NMT is based on data-driven methods. This means that SMT is more dependent on the quality and quantity of the parallel corpus, while NMT is more dependent on the complexity of the neural network architecture and the amount of data used to train it.

Generally, NMT systems tend to produce translations that are more natural-sounding and accurate than SMT systems, but they require more computational resources to train and use.

Examples of SMT tools include:

• Moses
• SYSTRAN
• Google Translate: an online translation service that uses SMT to translate text between multiple languages.

Examples of NMT tools include:

• Google Translate
• OpenNMT
• Microsoft Translator
• DeepL
• Amazon Translate

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https://towardsdatascience.com/intuitive-explanation-of-neural-machine-translation-129789e3c59f

Intuitive explanation of Neural Machine Translation

 

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https://www.qblocks.cloud/blog/natural-language-processing-machine-translation#:~:text=In%20short%2C%20NLP%20gives%20the,the%20text%20from%20various%20languages.

Natural language processing (NLP) and its use in machine translation

Neural Machine Translation (NMT): 2014-Present

NMT is a popular and widely used translation service that incorporates an end-to-end approach for automatic translation which overcomes the weaknesses of RBMT and SMT methods. NMT uses the most recent deep learning methods to produce better translation output than other traditional Machine Translation solutions. It is the most recent type of machine translation that employs a neural network that is closely related to the neurons of the human brain, allowing it to categorize data into various groups and layers. NMT is a language translation approach that tries to incorporate the context of the sentences or paragraphs rather than individual words. The NMT system is made up of current multilingual databases and automated learning mechanisms that contribute to continuous improvement.

Advantages:

• End-to-end models (no pipeline of specific tasks)

Disadvantages:

• Requires a bilingual corpus
• Sometimes unable to resolve unfamiliar words.

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https://www.analyticssteps.com/blogs/4-types-machine-translation-nlp

4 Types of Machine Translation in NLP | Analytics Steps

Neural Machine Translation or NMT

NMT is a type of machine translation that relies upon neural network models (based on the human brain) to build statistical models with the end goal of translation. The essential advantage of NMT is that it gives a solitary system that can be prepared to unravel the source and target text. Subsequently, it doesn't rely upon specific systems that are regular to other machine translation systems, particularly SMT.

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Exploring Natural Language Processing (NLP)

NLP and Machine Translation

With thousands of different languages used all over the world, translation is one of the most exciting applications of natural language processing technology.

What is Machine Translation?

Machine translation (MT) is one of the oldest fields of AI research, and recent advancements in NLP have led to big improvements in translation quality. Machine translation is the process that a computer uses to translate text from one language to another, like English to Spanish, without human intervention.

The earliest versions of machine translation were plagued with inaccuracies and translation errors. In recent years, developments like neural machine translation (NMT) helped AI tools build their knowledge to craft more nuanced and accurate sentences. Google Translate, Microsoft Translate, DeepL, and IBM’s Watson use the latest NLP technology to power their machine translation systems.

Benefits of NLP Machine Translation

AI tools make the translation process easier than ever. A crucial benefit of machine translation is its lightning-fast processing power. Computers can now translate an entire book, website, or product database in mere seconds. Another primary benefit is cost. Many leading AI translation tools have low-cost enterprise versions for companies who wish to localize their websites.

Although NLP led to huge advancements in language translation, AI translations aren’t quite perfect yet. Machine translation can’t always understand cultural differences or translation context like human readers can. For this reason, human oversight is still needed for accurately translating content from one language to another.

In the translation industry, machine translation offers the most value as a tool to speed up the localization process. When used together with human post-editing, you get the best of both worlds.