What is Neural Machine Translation (NMT)? A Brief Overview
By: Shahzad Bashir
Neural machine translation is a tool to automate translation through an end-to-end learning mode. It is a neural network of machines that works on the encoding and decoding of the source text. It is more like running a set of predefined rules.
Neural machine translation as known as NMT has been playing a vital role in the quality of the translation and also has the ability to address traditional idioms and phrase-based content.
The neural machine is one unique discovery and invention which is a great tool and plays an important role in the accuracy and precision of the translation.
Before going into further details of machine translation to understand in general its other forms and also how it works, it's important to understand it.
Table of Content
- Definition of MT
- Types of Machine Translation
- What Makes Neural Machine Translation Different?
- Functioning of the Neural Machine Translation
- What do We Call a Neural Network?
- Advantages of the Neural Machine Translation
- Important Use Cases for Neural Machine
- What the Future Holds for Neural Machine Translation
- Final words
Definition of MT
Machine translation is a process in which artificial intelligence takes the charge and translates the content from one language to another language without human input. It’s a computer program that performs the translation from the source language to the target language without any human translator intervening.
This initially happened back in the early 1950s when experts performed this automatic translation test. However, this practice got regular during the 2000s with the statistical methods used and it started working.
The quality of translation that the machine started to offer was quite basic and apparent. That is because the machine requires a lot of effort.
Machine translation has become much more advanced and improved now with artificial intelligence, it was not much in practice in the early days.
Machine translation models had this mechanism where developers manually defined and applied the program rules.
Types of Machine Translation
Machine translation has two general subtypes which are
◘ Rule-based Machine Translation
This is an ancient form of machine translation and is now obsolete. It used to depend on the linguistic information about the source and target languages.
Human linguists define the rules for using grammar structure, word order phrases for data entry, and a few other factors.
Moreover, this system after having sufficient data from dictionaries system maps the source language to make it ready for the target language.
◘ Statistical Machine Translation
The statistical models enable the programs to generate hypothetical ideas about how they can translate the text in the future. The resources which are needed for the model training are large and huge.
You require a lot of words to train and tame the engine for the specific domain. It could be hectic, results however are good, particularly in technical and scientific texts.
What Makes Neural Machine Translation Different?
Neural network models differ from phrase and idiom-based systems. The phrase-based systems break the input sentence into a set of words and phrases which further map each word in the target language. Neural networks, however, take into account one whole sentence stepwise and further generates the output sentence.
Google translate made the changes and switched its translation network to neural machine translation naming it as Google neural machine translation system (GNMT). Google states how this change has been made to address the engineering and design choices while working on accuracy and speed.
Functioning of the Neural Machine Translation
This is a neural text that neural machine translation uses to translate the source text to the target text. Neural networks can work without much supervision and can also handle large datasets.
There are two main sections of the neural machine translation system and both of these are neural networks.
1. Encoder network
2. Decoder network
What do We Call a Neural Network?
An interconnected series of nodes that are loosely modeled on the human brain comprises a neural network. It contains an information system that enables input data to pass through the nodes to produce outputs.
It is a kind of neural network that is named neural network architecture and it is called a sequence-to-sequence neural network (Seq2Seq). It works with the source language sentence and then works on producing the corresponding target language sentences.
Advantages of the Neural Machine Translation
NMT is a powerful tool and has a great influence on machine translation. Its power resides in the neural architecture network. It is the one that allows one to learn from vast amounts of data and adapting new contexts as per requirement.
This is why neural machine translation is taken as an ideal technology for companies that require for translation of a big volume of text quite quickly, accurately, and conveniently.
There are numerous benefits of neural machine translation however, a few of these could be summed up as
◘ Quick learning
It barely takes a few sessions of the automated processes to learn and understand the neural networks and to have its training. The process is quite simple and quick, unlike the general machine translation which requires manual methods to learn and get trained.
◘ High accuracy
NMT is quite good with contexts that machine translation quite lacked initially. It can work on extensive data sets and can use language modeling. Moreover, it can also understand the contexts of sentences and phrases on a broader level working on delivering an accurate and fluent translation with minimal time required. On contrary, conventional machine translation only understands and considers the context of particular words.
◘ Integration and flexibility
Another important benefit of neural machine networks includes statistical predecessors and how they can get integrated with APIs and SDKs into further software and can also be applied to a lot of content and its file formats.
In an era where human translators get to cost an arm and a leg, NMT allows you to produce a lot more words that are too accurate and quickly with a fraction of the cost. However, human translators can always be hired for post-machine translation editing and there are particular tasks that only they can do.
Empowering people with customization is a great bliss and neural machine translation does that. It is quite possible to customize the output of NMT and update the model through technological databases, brand-specific glossaries, and a lot of data sources to have better results.
Where there is a need for translation scaled-up neural machine translation is the solution. It can make it quickly go up and meet the increased demand for translation.
All these important benefits of machine translation make it quite clear and obvious how influencing and empowering this network is. It can revolutionize the organization and its translation capabilities.
However, it is important to take note that neural machine translation doesn’t get fit all kinds of content needs and types. There are certain content needs and cases for which it works best and companies can make the most of it.
Important Use Cases for Neural Machine
Neural machine translation works best in particular scenarios. While a lot of content types including creative advertising copy designed should be left with the human translators for maximum impact and neural machine translation can handle it.
◘ Translating a large Volume of Text in a Short Time
NMT is quite efficient and empowering in that it can produce astonishingly precise and accurate translations out of ingesting a large amount of high-quality training data and that too without lending human help and in record time.
A relevant example may include translating a huge amount of content in a limited time could be the instant aftermath of some natural disaster. A lot of organizations including the French red cross often required to translate the content within hours to convey to people the latest developments all across the borders.
The proliferation of online customer reviews could be taken as another example. This content due to high volume presents a huge challenge for companies who are relying on user-created content for their marketing campaigns as they have to translate the content quickly and accurately before their customers divert to the competitors.
Translation of Repetitive Text
This is another case that can be done in minutes through neural machine translation. Machine translation in general and NMT are quite effective when it comes to meeting a requirement when you need a repetitive text time and again. These may include the
2. User instructions
3. User guides
4. And reference materials
For instance, a neural machine translation can gather and translate the data from existing human translations of product manuals in multiple languages. It can further go on to produce a high-quality translation of other correlated product manuals on occasions to come.
Translation of User-Generated Content for Multiple Analysis
The only way to process the hundreds and thousands of user-generated comments overnight and deliver actionable and accurate results in a limited time is a neural machine translation.
This is why brands take huge advantage of it and look for options to instantly translate the content which people post for marketing purposes.
This helps them to have an interaction with the people which is more meaningful all around the world and which is undoubtedly an effective global marketing strategy.
It can help to filter out the most prevalent sentiments among the customer base so that the brands and companies can quickly adapt the content and fit their products’ marketing messaging strategy according to the content and comments shared.
Online Customer Services
NMT can further be very useful for the helpdesk and customer services operations where at times the online representatives and staff members have to instantly and accurately translate the requests that they receive from people all around the world.
All those organizations who plan to save on the live chat agent can turn to neural machine translation to enhance the agents' capacity with the improvement of their customer’s experience. This is because no one else other than the neural machine network can enable the agents who communicate with customers quickly with customers who speak other languages.
Accuracy in machine translation
The performance of NMT varies with particular factors. It depends on the engine, language pair, and the training data available. It also depends on the text which is being translated.
The quality is big time relying on the number and amount of translations that an engine performs for a particular domain or language, it further helps to produce higher quality output.
It is important for the experts to take note that while taking the services of an NMT, it is imperative to select an optimal engine that has been selected to translate the content and this step is the key to having an accurate translation.
It could be tough for a layman to understand the differences between popular machine translation images to understand. Machine translation reports are also there to help with the detailed and up-to-date breakdown.
It further analyzes Google, Amazon, DeepL, and Microsoft translation engines. As they get to have an idea of how each engine is performing for particular languages, language pairs, and domains.
What the Future Holds for Neural Machine Translation
The future of neural translation seems promising and bright, with more upgrades in the machines, its features, and specialties which are only going to become better emerging this software is one of the greatest machine translations than ever. As artificial intelligence is also evolving and neural networks are becoming larger, huge, and more complex.
However, human translators should not feel insecure as the human translator role is barely going to be obsolete. A certain type of content and text with particular communication patterns can only be translated by a human being and machine translation can not excel in it.
Neural machine translation with its evolution and promising future will yet have to struggle with particular content types to meet the accuracy level that is delivered by human translators. It might have to take a long to get good at translating the nuanced expressions.
Most translation experts and linguists agree that NMT can only shine in future with the human translation skills and capabilities. They will always be relying on human creativity, analytical skills, and nuanced interpretation.
Neural machine translation is an advanced version of machine translation and is quite efficient in its functioning. It can deliver an accurate and context-based translation. However, there are particular content cases for which NMT is more effective.
It has a lot of benefits with high accuracy, quick learning, scalability, flexibility, and a lot more. NMT has a bright and promising future with more involvement and the evolution of artificial intelligence.
Human translators nonetheless should never get insecure as there are certain content cases that can only be handled by them.